Overview

Dataset statistics

Number of variables28
Number of observations131
Missing cells73
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.8 KiB
Average record size in memory225.0 B

Variable types

Numeric9
Categorical19

Alerts

airdate has constant value "2020-12-31" Constant
url has a high cardinality: 131 distinct values High cardinality
name has a high cardinality: 106 distinct values High cardinality
_embedded_show_url has a high cardinality: 71 distinct values High cardinality
_embedded_show_name has a high cardinality: 71 distinct values High cardinality
_embedded_show_premiered has a high cardinality: 56 distinct values High cardinality
_embedded_show_officialSite has a high cardinality: 62 distinct values High cardinality
_embedded_show_summary has a high cardinality: 61 distinct values High cardinality
_links_self_href has a high cardinality: 131 distinct values High cardinality
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
season is highly correlated with number and 1 other fieldsHigh correlation
number is highly correlated with season and 2 other fieldsHigh correlation
runtime is highly correlated with number and 2 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with season and 3 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
runtime is highly correlated with _embedded_show_runtime and 1 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with runtime and 1 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with _embedded_show_ended and 12 other fieldsHigh correlation
_embedded_show_ended is highly correlated with _embedded_show_officialSite and 7 other fieldsHigh correlation
_embedded_show_name is highly correlated with _embedded_show_officialSite and 13 other fieldsHigh correlation
summary is highly correlated with airdateHigh correlation
_embedded_show_summary is highly correlated with _embedded_show_officialSite and 12 other fieldsHigh correlation
_embedded_show_genres is highly correlated with _embedded_show_officialSite and 10 other fieldsHigh correlation
type is highly correlated with _embedded_show_officialSite and 5 other fieldsHigh correlation
_embedded_show_type is highly correlated with _embedded_show_officialSite and 5 other fieldsHigh correlation
airdate is highly correlated with _embedded_show_officialSite and 14 other fieldsHigh correlation
airtime is highly correlated with _embedded_show_officialSite and 8 other fieldsHigh correlation
_embedded_show_status is highly correlated with _embedded_show_ended and 6 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with _embedded_show_officialSite and 11 other fieldsHigh correlation
_embedded_show_url is highly correlated with _embedded_show_officialSite and 13 other fieldsHigh correlation
_embedded_show_language is highly correlated with _embedded_show_officialSite and 6 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with _embedded_show_officialSite and 7 other fieldsHigh correlation
airstamp is highly correlated with _embedded_show_officialSite and 7 other fieldsHigh correlation
id is highly correlated with season and 17 other fieldsHigh correlation
season is highly correlated with id and 10 other fieldsHigh correlation
number is highly correlated with id and 10 other fieldsHigh correlation
type is highly correlated with airtime and 10 other fieldsHigh correlation
airtime is highly correlated with type and 13 other fieldsHigh correlation
airstamp is highly correlated with id and 18 other fieldsHigh correlation
runtime is highly correlated with season and 14 other fieldsHigh correlation
summary is highly correlated with _embedded_show_id and 2 other fieldsHigh correlation
_embedded_show_id is highly correlated with id and 20 other fieldsHigh correlation
_embedded_show_url is highly correlated with id and 21 other fieldsHigh correlation
_embedded_show_name is highly correlated with id and 21 other fieldsHigh correlation
_embedded_show_type is highly correlated with id and 17 other fieldsHigh correlation
_embedded_show_language is highly correlated with id and 18 other fieldsHigh correlation
_embedded_show_genres is highly correlated with id and 19 other fieldsHigh correlation
_embedded_show_status is highly correlated with id and 17 other fieldsHigh correlation
_embedded_show_runtime is highly correlated with id and 17 other fieldsHigh correlation
_embedded_show_averageRuntime is highly correlated with season and 17 other fieldsHigh correlation
_embedded_show_premiered is highly correlated with id and 21 other fieldsHigh correlation
_embedded_show_ended is highly correlated with id and 17 other fieldsHigh correlation
_embedded_show_officialSite is highly correlated with id and 21 other fieldsHigh correlation
_embedded_show_weight is highly correlated with id and 16 other fieldsHigh correlation
_embedded_show_dvdCountry is highly correlated with id and 10 other fieldsHigh correlation
_embedded_show_summary is highly correlated with id and 21 other fieldsHigh correlation
_embedded_show_updated is highly correlated with id and 15 other fieldsHigh correlation
number has 5 (3.8%) missing values Missing
runtime has 14 (10.7%) missing values Missing
_embedded_show_runtime has 42 (32.1%) missing values Missing
_embedded_show_averageRuntime has 12 (9.2%) missing values Missing
url is uniformly distributed Uniform
name is uniformly distributed Uniform
_links_self_href is uniformly distributed Uniform
id has unique values Unique
url has unique values Unique
_links_self_href has unique values Unique
_embedded_show_weight has 6 (4.6%) zeros Zeros

Reproduction

Analysis started2022-05-10 02:24:16.837793
Analysis finished2022-05-10 02:24:50.773975
Duration33.94 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2046277.969
Minimum1949329
Maximum2324433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-09T21:24:50.854152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1949329
5-th percentile1949335.5
Q11996544
median2001669
Q32039622.5
95-th percentile2312227.5
Maximum2324433
Range375104
Interquartile range (IQR)43078.5

Descriptive statistics

Standard deviation106247.1195
Coefficient of variation (CV)0.05192213427
Kurtosis1.80260106
Mean2046277.969
Median Absolute Deviation (MAD)16185
Skewness1.759037673
Sum268062414
Variance1.12884504 × 1010
MonotonicityNot monotonic
2022-05-09T21:24:50.979404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19779021
 
0.8%
20016691
 
0.8%
20016671
 
0.8%
20016661
 
0.8%
20016651
 
0.8%
20000731
 
0.8%
20000721
 
0.8%
19975381
 
0.8%
19975371
 
0.8%
19884051
 
0.8%
Other values (121)121
92.4%
ValueCountFrequency (%)
19493291
0.8%
19493301
0.8%
19493311
0.8%
19493321
0.8%
19493331
0.8%
19493341
0.8%
19493351
0.8%
19493361
0.8%
19503691
0.8%
19507031
0.8%
ValueCountFrequency (%)
23244331
0.8%
23244321
0.8%
23244301
0.8%
23244291
0.8%
23244281
0.8%
23244271
0.8%
23122281
0.8%
23122271
0.8%
23122261
0.8%
23122251
0.8%

url
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
https://www.tvmaze.com/episodes/1977902/obycnaa-zensina-2x06-seria-15
 
1
https://www.tvmaze.com/episodes/2001669/three-men-four-wheels-1x05-episode-5
 
1
https://www.tvmaze.com/episodes/2001667/three-men-four-wheels-1x03-episode-3
 
1
https://www.tvmaze.com/episodes/2001666/three-men-four-wheels-1x02-episode-2
 
1
https://www.tvmaze.com/episodes/2001665/three-men-four-wheels-1x01-episode-1
 
1
Other values (126)
126 

Length

Max length145
Median length105
Mean length81.61068702
Min length59

Characters and Unicode

Total characters10691
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique131 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/1977902/obycnaa-zensina-2x06-seria-15
2nd rowhttps://www.tvmaze.com/episodes/2015818/po-sezonu-videodajdzest-seasonvar-6x53-vypusk-307
3rd rowhttps://www.tvmaze.com/episodes/1964000/257-pricin-ctoby-zit-2x10-seria-23
4th rowhttps://www.tvmaze.com/episodes/1995405/proekt-anna-nikolaevna-s02-special-nacalo
5th rowhttps://www.tvmaze.com/episodes/2007760/stand-up-autsajd-1x12-aroslav-kondrasov

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1977902/obycnaa-zensina-2x06-seria-151
 
0.8%
https://www.tvmaze.com/episodes/2001669/three-men-four-wheels-1x05-episode-51
 
0.8%
https://www.tvmaze.com/episodes/2001667/three-men-four-wheels-1x03-episode-31
 
0.8%
https://www.tvmaze.com/episodes/2001666/three-men-four-wheels-1x02-episode-21
 
0.8%
https://www.tvmaze.com/episodes/2001665/three-men-four-wheels-1x01-episode-11
 
0.8%
https://www.tvmaze.com/episodes/2000073/ultimate-note-1x26-episode-261
 
0.8%
https://www.tvmaze.com/episodes/2000072/ultimate-note-1x25-episode-251
 
0.8%
https://www.tvmaze.com/episodes/1997538/the-penalty-zone-1x30-episode-301
 
0.8%
https://www.tvmaze.com/episodes/1997537/the-penalty-zone-1x29-episode-291
 
0.8%
https://www.tvmaze.com/episodes/1988405/love-teenager-1x03-my-best-friends-guy-friend-likes-me1
 
0.8%
Other values (121)121
92.4%

Length

2022-05-09T21:24:51.110361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/1977902/obycnaa-zensina-2x06-seria-151
 
0.8%
https://www.tvmaze.com/episodes/2312225/unsolved-cases-of-kung-fu-portrait-of-beauty-1x09-episode-91
 
0.8%
https://www.tvmaze.com/episodes/1964000/257-pricin-ctoby-zit-2x10-seria-231
 
0.8%
https://www.tvmaze.com/episodes/1995405/proekt-anna-nikolaevna-s02-special-nacalo1
 
0.8%
https://www.tvmaze.com/episodes/2007760/stand-up-autsajd-1x12-aroslav-kondrasov1
 
0.8%
https://www.tvmaze.com/episodes/1985789/theres-a-pit-in-my-senior-martial-brothers-brain-2x11-episode-111
 
0.8%
https://www.tvmaze.com/episodes/2039622/witches-1x05-episode-51
 
0.8%
https://www.tvmaze.com/episodes/2039623/witches-1x06-episode-61
 
0.8%
https://www.tvmaze.com/episodes/2324427/unique-lady-2x15-episode-151
 
0.8%
https://www.tvmaze.com/episodes/2324428/unique-lady-2x16-episode-161
 
0.8%
Other values (121)121
92.4%

Most occurring characters

ValueCountFrequency (%)
e971
 
9.1%
-846
 
7.9%
s693
 
6.5%
/655
 
6.1%
t635
 
5.9%
o537
 
5.0%
w446
 
4.2%
i409
 
3.8%
a402
 
3.8%
p376
 
3.5%
Other values (30)4721
44.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter7305
68.3%
Decimal Number1492
 
14.0%
Other Punctuation1048
 
9.8%
Dash Punctuation846
 
7.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e971
13.3%
s693
 
9.5%
t635
 
8.7%
o537
 
7.4%
w446
 
6.1%
i409
 
5.6%
a402
 
5.5%
p376
 
5.1%
m354
 
4.8%
d322
 
4.4%
Other values (16)2160
29.6%
Decimal Number
ValueCountFrequency (%)
1263
17.6%
2245
16.4%
0240
16.1%
9158
10.6%
3127
8.5%
697
 
6.5%
894
 
6.3%
792
 
6.2%
491
 
6.1%
585
 
5.7%
Other Punctuation
ValueCountFrequency (%)
/655
62.5%
.262
 
25.0%
:131
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-846
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin7305
68.3%
Common3386
31.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e971
13.3%
s693
 
9.5%
t635
 
8.7%
o537
 
7.4%
w446
 
6.1%
i409
 
5.6%
a402
 
5.5%
p376
 
5.1%
m354
 
4.8%
d322
 
4.4%
Other values (16)2160
29.6%
Common
ValueCountFrequency (%)
-846
25.0%
/655
19.3%
1263
 
7.8%
.262
 
7.7%
2245
 
7.2%
0240
 
7.1%
9158
 
4.7%
:131
 
3.9%
3127
 
3.8%
697
 
2.9%
Other values (4)362
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII10691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e971
 
9.1%
-846
 
7.9%
s693
 
6.5%
/655
 
6.1%
t635
 
5.9%
o537
 
5.0%
w446
 
4.2%
i409
 
3.8%
a402
 
3.8%
p376
 
3.5%
Other values (30)4721
44.2%

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct106
Distinct (%)80.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Episode 7
 
7
Episode 8
 
4
Episode 9
 
3
Episode 5
 
3
Episode 15
 
3
Other values (101)
111 

Length

Max length60
Median length53
Mean length17.17557252
Min length6

Characters and Unicode

Total characters2250
Distinct characters135
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)69.5%

Sample

1st rowСерия 15
2nd rowВыпуск 307
3rd rowСерия 23
4th rowНачало
5th rowЯрослав Кондрашов

Common Values

ValueCountFrequency (%)
Episode 77
 
5.3%
Episode 84
 
3.1%
Episode 93
 
2.3%
Episode 53
 
2.3%
Episode 153
 
2.3%
Episode 172
 
1.5%
Episode 202
 
1.5%
Episode 42
 
1.5%
Episode 182
 
1.5%
Episode 192
 
1.5%
Other values (96)101
77.1%

Length

2022-05-09T21:24:51.236067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
episode55
 
13.2%
the24
 
5.7%
78
 
1.9%
of8
 
1.9%
chapter8
 
1.9%
7
 
1.7%
84
 
1.0%
154
 
1.0%
with4
 
1.0%
i4
 
1.0%
Other values (235)292
69.9%

Most occurring characters

ValueCountFrequency (%)
287
 
12.8%
e209
 
9.3%
i123
 
5.5%
o112
 
5.0%
s101
 
4.5%
d92
 
4.1%
r80
 
3.6%
p72
 
3.2%
a72
 
3.2%
t71
 
3.2%
Other values (125)1031
45.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1453
64.6%
Uppercase Letter307
 
13.6%
Space Separator287
 
12.8%
Decimal Number134
 
6.0%
Other Punctuation39
 
1.7%
Other Letter12
 
0.5%
Dash Punctuation10
 
0.4%
Connector Punctuation2
 
0.1%
Open Punctuation2
 
0.1%
Close Punctuation2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e209
14.4%
i123
 
8.5%
o112
 
7.7%
s101
 
7.0%
d92
 
6.3%
r80
 
5.5%
p72
 
5.0%
a72
 
5.0%
t71
 
4.9%
n67
 
4.6%
Other values (48)454
31.2%
Uppercase Letter
ValueCountFrequency (%)
E62
20.2%
T37
 
12.1%
M16
 
5.2%
D15
 
4.9%
S14
 
4.6%
I11
 
3.6%
B11
 
3.6%
W11
 
3.6%
F10
 
3.3%
C10
 
3.3%
Other values (35)110
35.8%
Decimal Number
ValueCountFrequency (%)
132
23.9%
222
16.4%
714
10.4%
313
9.7%
012
 
9.0%
510
 
7.5%
610
 
7.5%
88
 
6.0%
47
 
5.2%
96
 
4.5%
Other Letter
ValueCountFrequency (%)
ي2
16.7%
ل2
16.7%
ا2
16.7%
ه1
8.3%
ة1
8.3%
ب1
8.3%
ن1
8.3%
و1
8.3%
س1
8.3%
Other Punctuation
ValueCountFrequency (%)
.15
38.5%
:8
20.5%
,6
 
15.4%
'6
 
15.4%
!2
 
5.1%
1
 
2.6%
/1
 
2.6%
Space Separator
ValueCountFrequency (%)
287
100.0%
Dash Punctuation
ValueCountFrequency (%)
-10
100.0%
Connector Punctuation
ValueCountFrequency (%)
_2
100.0%
Open Punctuation
ValueCountFrequency (%)
(2
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%
Math Symbol
ValueCountFrequency (%)
|2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1552
69.0%
Common478
 
21.2%
Cyrillic208
 
9.2%
Arabic12
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e209
 
13.5%
i123
 
7.9%
o112
 
7.2%
s101
 
6.5%
d92
 
5.9%
r80
 
5.2%
p72
 
4.6%
a72
 
4.6%
t71
 
4.6%
n67
 
4.3%
Other values (49)553
35.6%
Cyrillic
ValueCountFrequency (%)
о24
 
11.5%
а19
 
9.1%
в15
 
7.2%
р10
 
4.8%
и9
 
4.3%
е9
 
4.3%
д9
 
4.3%
л8
 
3.8%
Г8
 
3.8%
н7
 
3.4%
Other values (34)90
43.3%
Common
ValueCountFrequency (%)
287
60.0%
132
 
6.7%
222
 
4.6%
.15
 
3.1%
714
 
2.9%
313
 
2.7%
012
 
2.5%
-10
 
2.1%
510
 
2.1%
610
 
2.1%
Other values (13)53
 
11.1%
Arabic
ValueCountFrequency (%)
ي2
16.7%
ل2
16.7%
ا2
16.7%
ه1
8.3%
ة1
8.3%
ب1
8.3%
ن1
8.3%
و1
8.3%
س1
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2017
89.6%
Cyrillic208
 
9.2%
None12
 
0.5%
Arabic12
 
0.5%
Punctuation1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
287
 
14.2%
e209
 
10.4%
i123
 
6.1%
o112
 
5.6%
s101
 
5.0%
d92
 
4.6%
r80
 
4.0%
p72
 
3.6%
a72
 
3.6%
t71
 
3.5%
Other values (63)798
39.6%
Cyrillic
ValueCountFrequency (%)
о24
 
11.5%
а19
 
9.1%
в15
 
7.2%
р10
 
4.8%
и9
 
4.3%
е9
 
4.3%
д9
 
4.3%
л8
 
3.8%
Г8
 
3.8%
н7
 
3.4%
Other values (34)90
43.3%
None
ValueCountFrequency (%)
ı3
25.0%
ö3
25.0%
ş1
 
8.3%
Ö1
 
8.3%
ü1
 
8.3%
é1
 
8.3%
ç1
 
8.3%
ã1
 
8.3%
Arabic
ValueCountFrequency (%)
ي2
16.7%
ل2
16.7%
ا2
16.7%
ه1
8.3%
ة1
8.3%
ب1
8.3%
ن1
8.3%
و1
8.3%
س1
8.3%
Punctuation
ValueCountFrequency (%)
1
100.0%

season
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.9694656
Minimum1
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-09T21:24:51.330140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile2020
Maximum2020
Range2019
Interquartile range (IQR)2

Descriptive statistics

Standard deviation484.9189296
Coefficient of variation (CV)3.849495805
Kurtosis11.93271429
Mean125.9694656
Median Absolute Deviation (MAD)1
Skewness3.707925458
Sum16502
Variance235146.3683
MonotonicityNot monotonic
2022-05-09T21:24:51.410520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
164
48.9%
225
 
19.1%
312
 
9.2%
411
 
8.4%
20208
 
6.1%
53
 
2.3%
62
 
1.5%
81
 
0.8%
71
 
0.8%
511
 
0.8%
Other values (3)3
 
2.3%
ValueCountFrequency (%)
164
48.9%
225
 
19.1%
312
 
9.2%
411
 
8.4%
53
 
2.3%
62
 
1.5%
71
 
0.8%
81
 
0.8%
91
 
0.8%
151
 
0.8%
ValueCountFrequency (%)
20208
6.1%
511
 
0.8%
311
 
0.8%
151
 
0.8%
91
 
0.8%
81
 
0.8%
71
 
0.8%
62
 
1.5%
53
 
2.3%
411
8.4%

number
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct38
Distinct (%)30.2%
Missing5
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean20.30952381
Minimum1
Maximum358
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-09T21:24:51.519966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median8
Q316.75
95-th percentile53
Maximum358
Range357
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation51.05047922
Coefficient of variation (CV)2.513622658
Kurtosis33.90343148
Mean20.30952381
Median Absolute Deviation (MAD)4
Skewness5.761386224
Sum2559
Variance2606.151429
MonotonicityNot monotonic
2022-05-09T21:24:51.620204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
711
 
8.4%
810
 
7.6%
38
 
6.1%
48
 
6.1%
28
 
6.1%
58
 
6.1%
67
 
5.3%
96
 
4.6%
16
 
4.6%
106
 
4.6%
Other values (28)48
36.6%
ValueCountFrequency (%)
16
4.6%
28
6.1%
38
6.1%
48
6.1%
58
6.1%
67
5.3%
711
8.4%
810
7.6%
96
4.6%
106
4.6%
ValueCountFrequency (%)
3581
 
0.8%
3241
 
0.8%
3231
 
0.8%
721
 
0.8%
551
 
0.8%
533
2.3%
501
 
0.8%
381
 
0.8%
371
 
0.8%
341
 
0.8%

type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
regular
126 
insignificant_special
 
3
significant_special
 
2

Length

Max length21
Median length7
Mean length7.503816794
Min length7

Characters and Unicode

Total characters983
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowsignificant_special
5th rowregular

Common Values

ValueCountFrequency (%)
regular126
96.2%
insignificant_special3
 
2.3%
significant_special2
 
1.5%

Length

2022-05-09T21:24:51.738250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:24:51.843492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
regular126
96.2%
insignificant_special3
 
2.3%
significant_special2
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r252
25.6%
a136
13.8%
e131
13.3%
g131
13.3%
l131
13.3%
u126
12.8%
i23
 
2.3%
n13
 
1.3%
s10
 
1.0%
c10
 
1.0%
Other values (4)20
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter978
99.5%
Connector Punctuation5
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r252
25.8%
a136
13.9%
e131
13.4%
g131
13.4%
l131
13.4%
u126
12.9%
i23
 
2.4%
n13
 
1.3%
s10
 
1.0%
c10
 
1.0%
Other values (3)15
 
1.5%
Connector Punctuation
ValueCountFrequency (%)
_5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin978
99.5%
Common5
 
0.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r252
25.8%
a136
13.9%
e131
13.4%
g131
13.4%
l131
13.4%
u126
12.9%
i23
 
2.4%
n13
 
1.3%
s10
 
1.0%
c10
 
1.0%
Other values (3)15
 
1.5%
Common
ValueCountFrequency (%)
_5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r252
25.6%
a136
13.8%
e131
13.3%
g131
13.3%
l131
13.3%
u126
12.8%
i23
 
2.3%
n13
 
1.3%
s10
 
1.0%
c10
 
1.0%
Other values (4)20
 
2.0%

airdate
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2020-12-31
131 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1310
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-12-31
2nd row2020-12-31
3rd row2020-12-31
4th row2020-12-31
5th row2020-12-31

Common Values

ValueCountFrequency (%)
2020-12-31131
100.0%

Length

2022-05-09T21:24:52.015921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:24:52.115189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-12-31131
100.0%

Most occurring characters

ValueCountFrequency (%)
2393
30.0%
0262
20.0%
-262
20.0%
1262
20.0%
3131
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1048
80.0%
Dash Punctuation262
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2393
37.5%
0262
25.0%
1262
25.0%
3131
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-262
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2393
30.0%
0262
20.0%
-262
20.0%
1262
20.0%
3131
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2393
30.0%
0262
20.0%
-262
20.0%
1262
20.0%
3131
 
10.0%

airtime
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
nan
98 
12:00
10 
21:00
 
9
20:00
 
3
19:00
 
2
Other values (9)
 
9

Length

Max length5
Median length3
Mean length3.503816794
Min length3

Characters and Unicode

Total characters459
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)6.9%

Sample

1st row10:00
2nd rownan
3rd rownan
4th rownan
5th row12:00

Common Values

ValueCountFrequency (%)
nan98
74.8%
12:0010
 
7.6%
21:009
 
6.9%
20:003
 
2.3%
19:002
 
1.5%
10:001
 
0.8%
11:001
 
0.8%
06:001
 
0.8%
17:001
 
0.8%
18:001
 
0.8%
Other values (4)4
 
3.1%

Length

2022-05-09T21:24:52.197704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan98
74.8%
12:0010
 
7.6%
21:009
 
6.9%
20:003
 
2.3%
19:002
 
1.5%
10:001
 
0.8%
11:001
 
0.8%
06:001
 
0.8%
17:001
 
0.8%
18:001
 
0.8%
Other values (4)4
 
3.1%

Most occurring characters

ValueCountFrequency (%)
n196
42.7%
a98
21.4%
070
 
15.3%
:33
 
7.2%
128
 
6.1%
225
 
5.4%
94
 
0.9%
52
 
0.4%
61
 
0.2%
71
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter294
64.1%
Decimal Number132
28.8%
Other Punctuation33
 
7.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
070
53.0%
128
 
21.2%
225
 
18.9%
94
 
3.0%
52
 
1.5%
61
 
0.8%
71
 
0.8%
81
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
n196
66.7%
a98
33.3%
Other Punctuation
ValueCountFrequency (%)
:33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin294
64.1%
Common165
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
070
42.4%
:33
20.0%
128
 
17.0%
225
 
15.2%
94
 
2.4%
52
 
1.2%
61
 
0.6%
71
 
0.6%
81
 
0.6%
Latin
ValueCountFrequency (%)
n196
66.7%
a98
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII459
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n196
42.7%
a98
21.4%
070
 
15.3%
:33
 
7.2%
128
 
6.1%
225
 
5.4%
94
 
0.9%
52
 
0.4%
61
 
0.2%
71
 
0.2%

airstamp
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2020-12-31T12:00:00+00:00
59 
2020-12-31T04:00:00+00:00
29 
2020-12-31T09:00:00+00:00
2021-01-01T02:00:00+00:00
2020-12-31T17:00:00+00:00
Other values (13)
22 

Length

Max length25
Median length25
Mean length25
Min length25

Characters and Unicode

Total characters3275
Distinct characters13
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)6.1%

Sample

1st row2020-12-30T22:00:00+00:00
2nd row2020-12-31T00:00:00+00:00
3rd row2020-12-31T00:00:00+00:00
4th row2020-12-31T00:00:00+00:00
5th row2020-12-31T00:00:00+00:00

Common Values

ValueCountFrequency (%)
2020-12-31T12:00:00+00:0059
45.0%
2020-12-31T04:00:00+00:0029
22.1%
2020-12-31T09:00:00+00:009
 
6.9%
2021-01-01T02:00:00+00:006
 
4.6%
2020-12-31T17:00:00+00:006
 
4.6%
2020-12-31T00:00:00+00:004
 
3.1%
2020-12-31T03:00:00+00:003
 
2.3%
2020-12-31T10:00:00+00:003
 
2.3%
2020-12-31T14:00:00+00:002
 
1.5%
2020-12-31T11:00:00+00:002
 
1.5%
Other values (8)8
 
6.1%

Length

2022-05-09T21:24:52.282045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-31t12:00:00+00:0059
45.0%
2020-12-31t04:00:00+00:0029
22.1%
2020-12-31t09:00:00+00:009
 
6.9%
2021-01-01t02:00:00+00:006
 
4.6%
2020-12-31t17:00:00+00:006
 
4.6%
2020-12-31t00:00:00+00:004
 
3.1%
2020-12-31t03:00:00+00:003
 
2.3%
2020-12-31t10:00:00+00:003
 
2.3%
2020-12-31t11:00:00+00:002
 
1.5%
2020-12-31t14:00:00+00:002
 
1.5%
Other values (8)8
 
6.1%

Most occurring characters

ValueCountFrequency (%)
01377
42.0%
2456
 
13.9%
:393
 
12.0%
1344
 
10.5%
-262
 
8.0%
T131
 
4.0%
+131
 
4.0%
3129
 
3.9%
431
 
0.9%
910
 
0.3%
Other values (3)11
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2358
72.0%
Other Punctuation393
 
12.0%
Dash Punctuation262
 
8.0%
Uppercase Letter131
 
4.0%
Math Symbol131
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01377
58.4%
2456
 
19.3%
1344
 
14.6%
3129
 
5.5%
431
 
1.3%
910
 
0.4%
77
 
0.3%
82
 
0.1%
52
 
0.1%
Other Punctuation
ValueCountFrequency (%)
:393
100.0%
Dash Punctuation
ValueCountFrequency (%)
-262
100.0%
Uppercase Letter
ValueCountFrequency (%)
T131
100.0%
Math Symbol
ValueCountFrequency (%)
+131
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3144
96.0%
Latin131
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01377
43.8%
2456
 
14.5%
:393
 
12.5%
1344
 
10.9%
-262
 
8.3%
+131
 
4.2%
3129
 
4.1%
431
 
1.0%
910
 
0.3%
77
 
0.2%
Other values (2)4
 
0.1%
Latin
ValueCountFrequency (%)
T131
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3275
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01377
42.0%
2456
 
13.9%
:393
 
12.0%
1344
 
10.5%
-262
 
8.0%
T131
 
4.0%
+131
 
4.0%
3129
 
3.9%
431
 
0.9%
910
 
0.3%
Other values (3)11
 
0.3%

runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct38
Distinct (%)32.5%
Missing14
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean41.34188034
Minimum4
Maximum213
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-09T21:24:52.375874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile8.6
Q130
median42
Q351
95-th percentile60.6
Maximum213
Range209
Interquartile range (IQR)21

Descriptive statistics

Standard deviation25.98678789
Coefficient of variation (CV)0.6285826303
Kurtosis16.90757188
Mean41.34188034
Median Absolute Deviation (MAD)12
Skewness3.026005837
Sum4837
Variance675.3131447
MonotonicityNot monotonic
2022-05-09T21:24:52.470447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
4524
18.3%
3022
16.8%
6012
 
9.2%
408
 
6.1%
205
 
3.8%
1203
 
2.3%
103
 
2.3%
572
 
1.5%
582
 
1.5%
532
 
1.5%
Other values (28)34
26.0%
(Missing)14
10.7%
ValueCountFrequency (%)
42
1.5%
51
 
0.8%
62
1.5%
71
 
0.8%
91
 
0.8%
103
2.3%
121
 
0.8%
151
 
0.8%
171
 
0.8%
181
 
0.8%
ValueCountFrequency (%)
2131
 
0.8%
1203
 
2.3%
671
 
0.8%
631
 
0.8%
6012
9.2%
591
 
0.8%
582
 
1.5%
572
 
1.5%
561
 
0.8%
541
 
0.8%

summary
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct36
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
nan
96 
<p>Yun Woo recruits Yoo Han's help and together they search for his mother. As they dig deeper into the mystery surrounding her disappearance, an unexpected story begins to unfold. </p>
 
1
<p>The darkest days of the void arrive as Sabrina struggles with grief and regret. Can she summon the strength to overcome an endless cycle of destruction?</p>
 
1
<p>Mother Abagail selects a surprising voice to be her conduit to her chosen Committee in Boulder: the young deaf man Nick Andros. The arrival of a disturbing visitor to the Boulder Free Zone, shakes the Committee and Boulder residents to their core. Elsewhere in the Free Zone, Nadine Cross is haunted by a childhood memory.</p>
 
1
<p>This week, Director Jonathan Frakes joins host Wil Wheaton (Star Trek: The Next Generation) in "The Ready Room" to discuss Star Trek's complex new villain, the state of the distant future in the Star Trek Universe, and more!</p>
 
1
Other values (31)
31 

Length

Max length329
Median length3
Mean length39.58778626
Min length3

Characters and Unicode

Total characters5186
Distinct characters76
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)26.7%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan96
73.3%
<p>Yun Woo recruits Yoo Han's help and together they search for his mother. As they dig deeper into the mystery surrounding her disappearance, an unexpected story begins to unfold. </p>1
 
0.8%
<p>The darkest days of the void arrive as Sabrina struggles with grief and regret. Can she summon the strength to overcome an endless cycle of destruction?</p>1
 
0.8%
<p>Mother Abagail selects a surprising voice to be her conduit to her chosen Committee in Boulder: the young deaf man Nick Andros. The arrival of a disturbing visitor to the Boulder Free Zone, shakes the Committee and Boulder residents to their core. Elsewhere in the Free Zone, Nadine Cross is haunted by a childhood memory.</p>1
 
0.8%
<p>This week, Director Jonathan Frakes joins host Wil Wheaton (Star Trek: The Next Generation) in "The Ready Room" to discuss Star Trek's complex new villain, the state of the distant future in the Star Trek Universe, and more!</p>1
 
0.8%
<p>Tai's family finds a way to pay off their family's debt to Ms. Second.  Tien is even able to recover Tai's heirloom.  </p>1
 
0.8%
<p>The five friends take Ellen to the airport and something unexpected happens.</p>1
 
0.8%
<p>The Highest suspects that Dikahn is with someone then finds out who it may be. Lilo disrupts the status quo at the compound in several ways. Ruth plans an escape while Brian sneakily tests Lynn for drugs.</p>1
 
0.8%
<p>Suppressed emotions unleash and things come to a boil as revelations unravel in the season finale.</p>1
 
0.8%
<p>To protect his rich and strategic lands in France, the English king, Richard the Lionheart, decided to build an impregnable castle to bar the route along the Seine, thus asserting his supremacy in Normandy. Four years later, France's King Philip besieged the site at the head of an army of 6,000 men.</p>1
 
0.8%
Other values (26)26
 
19.8%

Length

2022-05-09T21:24:52.580577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan96
 
10.8%
the50
 
5.6%
and28
 
3.2%
a25
 
2.8%
to24
 
2.7%
of17
 
1.9%
in15
 
1.7%
an10
 
1.1%
as8
 
0.9%
is8
 
0.9%
Other values (464)605
68.3%

Most occurring characters

ValueCountFrequency (%)
750
14.5%
n477
 
9.2%
e446
 
8.6%
a405
 
7.8%
t287
 
5.5%
s265
 
5.1%
i264
 
5.1%
r252
 
4.9%
o247
 
4.8%
h175
 
3.4%
Other values (66)1618
31.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3900
75.2%
Space Separator757
 
14.6%
Uppercase Letter195
 
3.8%
Math Symbol160
 
3.1%
Other Punctuation149
 
2.9%
Decimal Number16
 
0.3%
Dash Punctuation7
 
0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n477
12.2%
e446
11.4%
a405
10.4%
t287
 
7.4%
s265
 
6.8%
i264
 
6.8%
r252
 
6.5%
o247
 
6.3%
h175
 
4.5%
p143
 
3.7%
Other values (16)939
24.1%
Uppercase Letter
ValueCountFrequency (%)
T21
 
10.8%
S20
 
10.3%
B15
 
7.7%
C15
 
7.7%
A14
 
7.2%
L13
 
6.7%
R12
 
6.2%
H11
 
5.6%
F9
 
4.6%
E9
 
4.6%
Other values (15)56
28.7%
Other Punctuation
ValueCountFrequency (%)
.62
41.6%
/41
27.5%
,26
17.4%
'11
 
7.4%
?3
 
2.0%
"2
 
1.3%
:2
 
1.3%
!1
 
0.7%
1
 
0.7%
Decimal Number
ValueCountFrequency (%)
05
31.2%
43
18.8%
22
 
12.5%
12
 
12.5%
71
 
6.2%
91
 
6.2%
81
 
6.2%
61
 
6.2%
Space Separator
ValueCountFrequency (%)
750
99.1%
 7
 
0.9%
Math Symbol
ValueCountFrequency (%)
<80
50.0%
>80
50.0%
Dash Punctuation
ValueCountFrequency (%)
-4
57.1%
3
42.9%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4095
79.0%
Common1091
 
21.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n477
11.6%
e446
 
10.9%
a405
 
9.9%
t287
 
7.0%
s265
 
6.5%
i264
 
6.4%
r252
 
6.2%
o247
 
6.0%
h175
 
4.3%
p143
 
3.5%
Other values (41)1134
27.7%
Common
ValueCountFrequency (%)
750
68.7%
<80
 
7.3%
>80
 
7.3%
.62
 
5.7%
/41
 
3.8%
,26
 
2.4%
'11
 
1.0%
 7
 
0.6%
05
 
0.5%
-4
 
0.4%
Other values (15)25
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5175
99.8%
None7
 
0.1%
Punctuation4
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
750
14.5%
n477
 
9.2%
e446
 
8.6%
a405
 
7.8%
t287
 
5.5%
s265
 
5.1%
i264
 
5.1%
r252
 
4.9%
o247
 
4.8%
h175
 
3.4%
Other values (63)1607
31.1%
None
ValueCountFrequency (%)
 7
100.0%
Punctuation
ValueCountFrequency (%)
3
75.0%
1
 
25.0%

_embedded_show_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct71
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43881.45038
Minimum2504
Maximum61556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-09T21:24:52.695930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2504
5-th percentile16001.5
Q138318
median51316
Q352847
95-th percentile60114
Maximum61556
Range59052
Interquartile range (IQR)14529

Descriptive statistics

Standard deviation14232.3068
Coefficient of variation (CV)0.324335378
Kurtosis0.3964382598
Mean43881.45038
Median Absolute Deviation (MAD)4628
Skewness-1.147194318
Sum5748470
Variance202558556.8
MonotonicityNot monotonic
2022-05-09T21:24:52.806177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1935510
 
7.6%
5284710
 
7.6%
326498
 
6.1%
414906
 
4.6%
615566
 
4.6%
527846
 
4.6%
394416
 
4.6%
593803
 
2.3%
538302
 
1.5%
529362
 
1.5%
Other values (61)72
55.0%
ValueCountFrequency (%)
25041
 
0.8%
65441
 
0.8%
74801
 
0.8%
75641
 
0.8%
78471
 
0.8%
152502
 
1.5%
167531
 
0.8%
170461
 
0.8%
1935510
7.6%
283811
 
0.8%
ValueCountFrequency (%)
615566
4.6%
608481
 
0.8%
593803
2.3%
575561
 
0.8%
562531
 
0.8%
554031
 
0.8%
552882
 
1.5%
551991
 
0.8%
546581
 
0.8%
546101
 
0.8%

_embedded_show_url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct71
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
https://www.tvmaze.com/shows/19355/loudermilk
10 
https://www.tvmaze.com/shows/52847/three-men-four-wheels
10 
https://www.tvmaze.com/shows/32649/chilling-adventures-of-sabrina
 
8
https://www.tvmaze.com/shows/41490/unique-lady
 
6
https://www.tvmaze.com/shows/61556/unsolved-cases-of-kung-fu-portrait-of-beauty
 
6
Other values (66)
91 

Length

Max length83
Median length61
Mean length53.16793893
Min length39

Characters and Unicode

Total characters6965
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)38.2%

Sample

1st rowhttps://www.tvmaze.com/shows/39115/obycnaa-zensina
2nd rowhttps://www.tvmaze.com/shows/7847/po-sezonu-videodajdzest-seasonvar
3rd rowhttps://www.tvmaze.com/shows/43722/257-pricin-ctoby-zit
4th rowhttps://www.tvmaze.com/shows/46688/proekt-anna-nikolaevna
5th rowhttps://www.tvmaze.com/shows/51065/stand-up-autsajd

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/shows/19355/loudermilk10
 
7.6%
https://www.tvmaze.com/shows/52847/three-men-four-wheels10
 
7.6%
https://www.tvmaze.com/shows/32649/chilling-adventures-of-sabrina8
 
6.1%
https://www.tvmaze.com/shows/41490/unique-lady6
 
4.6%
https://www.tvmaze.com/shows/61556/unsolved-cases-of-kung-fu-portrait-of-beauty6
 
4.6%
https://www.tvmaze.com/shows/52784/unique-lady-26
 
4.6%
https://www.tvmaze.com/shows/39441/carl-webers-the-family-business6
 
4.6%
https://www.tvmaze.com/shows/59380/forteresses-assiegees-batailles-de-legende3
 
2.3%
https://www.tvmaze.com/shows/53830/witches2
 
1.5%
https://www.tvmaze.com/shows/52936/my-best-friends-story2
 
1.5%
Other values (61)72
55.0%

Length

2022-05-09T21:24:52.952057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/shows/19355/loudermilk10
 
7.6%
https://www.tvmaze.com/shows/52847/three-men-four-wheels10
 
7.6%
https://www.tvmaze.com/shows/32649/chilling-adventures-of-sabrina8
 
6.1%
https://www.tvmaze.com/shows/41490/unique-lady6
 
4.6%
https://www.tvmaze.com/shows/61556/unsolved-cases-of-kung-fu-portrait-of-beauty6
 
4.6%
https://www.tvmaze.com/shows/52784/unique-lady-26
 
4.6%
https://www.tvmaze.com/shows/39441/carl-webers-the-family-business6
 
4.6%
https://www.tvmaze.com/shows/59380/forteresses-assiegees-batailles-de-legende3
 
2.3%
https://www.tvmaze.com/shows/15250/the-young-turks2
 
1.5%
https://www.tvmaze.com/shows/53698/the-shore2
 
1.5%
Other values (61)72
55.0%

Most occurring characters

ValueCountFrequency (%)
/655
 
9.4%
w558
 
8.0%
s555
 
8.0%
t522
 
7.5%
e422
 
6.1%
o391
 
5.6%
h331
 
4.8%
m315
 
4.5%
a289
 
4.1%
-275
 
3.9%
Other values (30)2652
38.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4982
71.5%
Other Punctuation1048
 
15.0%
Decimal Number660
 
9.5%
Dash Punctuation275
 
3.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w558
11.2%
s555
11.1%
t522
10.5%
e422
 
8.5%
o391
 
7.8%
h331
 
6.6%
m315
 
6.3%
a289
 
5.8%
c173
 
3.5%
v159
 
3.2%
Other values (16)1267
25.4%
Decimal Number
ValueCountFrequency (%)
5122
18.5%
492
13.9%
274
11.2%
367
10.2%
860
9.1%
156
8.5%
656
8.5%
954
8.2%
740
 
6.1%
039
 
5.9%
Other Punctuation
ValueCountFrequency (%)
/655
62.5%
.262
 
25.0%
:131
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-275
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4982
71.5%
Common1983
 
28.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
w558
11.2%
s555
11.1%
t522
10.5%
e422
 
8.5%
o391
 
7.8%
h331
 
6.6%
m315
 
6.3%
a289
 
5.8%
c173
 
3.5%
v159
 
3.2%
Other values (16)1267
25.4%
Common
ValueCountFrequency (%)
/655
33.0%
-275
13.9%
.262
 
13.2%
:131
 
6.6%
5122
 
6.2%
492
 
4.6%
274
 
3.7%
367
 
3.4%
860
 
3.0%
156
 
2.8%
Other values (4)189
 
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII6965
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/655
 
9.4%
w558
 
8.0%
s555
 
8.0%
t522
 
7.5%
e422
 
6.1%
o391
 
5.6%
h331
 
4.8%
m315
 
4.5%
a289
 
4.1%
-275
 
3.9%
Other values (30)2652
38.1%

_embedded_show_name
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct71
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Loudermilk
10 
Three Men Four Wheels
10 
Chilling Adventures of Sabrina
 
8
Unique Lady
 
6
Unsolved Cases of Kung Fu: Portrait of Beauty
 
6
Other values (66)
91 

Length

Max length50
Median length30
Mean length18.49618321
Min length4

Characters and Unicode

Total characters2423
Distinct characters102
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)38.2%

Sample

1st rowОбычная женщина
2nd rowПо сезону. Видеодайджест Seasonvar
3rd row257 причин, чтобы жить
4th rowПроект "Анна Николаевна"
5th rowStand Up Аутсайд

Common Values

ValueCountFrequency (%)
Loudermilk10
 
7.6%
Three Men Four Wheels10
 
7.6%
Chilling Adventures of Sabrina8
 
6.1%
Unique Lady6
 
4.6%
Unsolved Cases of Kung Fu: Portrait of Beauty6
 
4.6%
Unique Lady 26
 
4.6%
Carl Weber's The Family Business6
 
4.6%
Forteresses assiégées, batailles de légende3
 
2.3%
Witches2
 
1.5%
My Best Friend's Story2
 
1.5%
Other values (61)72
55.0%

Length

2022-05-09T21:24:53.055955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of23
 
5.7%
the18
 
4.4%
lady12
 
2.9%
unique12
 
2.9%
loudermilk10
 
2.5%
three10
 
2.5%
four10
 
2.5%
men10
 
2.5%
wheels10
 
2.5%
adventures8
 
2.0%
Other values (164)284
69.8%

Most occurring characters

ValueCountFrequency (%)
276
 
11.4%
e271
 
11.2%
r131
 
5.4%
n122
 
5.0%
s119
 
4.9%
a118
 
4.9%
i115
 
4.7%
o115
 
4.7%
l88
 
3.6%
u86
 
3.5%
Other values (92)982
40.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1733
71.5%
Uppercase Letter368
 
15.2%
Space Separator276
 
11.4%
Other Punctuation36
 
1.5%
Decimal Number10
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e271
15.6%
r131
 
7.6%
n122
 
7.0%
s119
 
6.9%
a118
 
6.8%
i115
 
6.6%
o115
 
6.6%
l88
 
5.1%
u86
 
5.0%
t76
 
4.4%
Other values (42)492
28.4%
Uppercase Letter
ValueCountFrequency (%)
T44
12.0%
L35
 
9.5%
S34
 
9.2%
F28
 
7.6%
C26
 
7.1%
W26
 
7.1%
U23
 
6.2%
M21
 
5.7%
B20
 
5.4%
A19
 
5.2%
Other values (28)92
25.0%
Other Punctuation
ValueCountFrequency (%)
'14
38.9%
:9
25.0%
,5
 
13.9%
!4
 
11.1%
"2
 
5.6%
&1
 
2.8%
.1
 
2.8%
Decimal Number
ValueCountFrequency (%)
27
70.0%
61
 
10.0%
71
 
10.0%
51
 
10.0%
Space Separator
ValueCountFrequency (%)
276
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1966
81.1%
Common322
 
13.3%
Cyrillic135
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e271
 
13.8%
r131
 
6.7%
n122
 
6.2%
s119
 
6.1%
a118
 
6.0%
i115
 
5.8%
o115
 
5.8%
l88
 
4.5%
u86
 
4.4%
t76
 
3.9%
Other values (45)725
36.9%
Cyrillic
ValueCountFrequency (%)
а15
 
11.1%
н12
 
8.9%
о10
 
7.4%
т9
 
6.7%
е8
 
5.9%
д7
 
5.2%
и7
 
5.2%
р5
 
3.7%
к5
 
3.7%
у5
 
3.7%
Other values (25)52
38.5%
Common
ValueCountFrequency (%)
276
85.7%
'14
 
4.3%
:9
 
2.8%
27
 
2.2%
,5
 
1.6%
!4
 
1.2%
"2
 
0.6%
&1
 
0.3%
.1
 
0.3%
61
 
0.3%
Other values (2)2
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2273
93.8%
Cyrillic135
 
5.6%
None15
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
276
 
12.1%
e271
 
11.9%
r131
 
5.8%
n122
 
5.4%
s119
 
5.2%
a118
 
5.2%
i115
 
5.1%
o115
 
5.1%
l88
 
3.9%
u86
 
3.8%
Other values (52)832
36.6%
Cyrillic
ValueCountFrequency (%)
а15
 
11.1%
н12
 
8.9%
о10
 
7.4%
т9
 
6.7%
е8
 
5.9%
д7
 
5.2%
и7
 
5.2%
р5
 
3.7%
к5
 
3.7%
у5
 
3.7%
Other values (25)52
38.5%
None
ValueCountFrequency (%)
é9
60.0%
Ş2
 
13.3%
ı2
 
13.3%
Ç1
 
6.7%
ğ1
 
6.7%

_embedded_show_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Scripted
89 
Reality
15 
Talk Show
 
7
Animation
 
7
Documentary
 
5
Other values (4)
 
8

Length

Max length11
Median length8
Mean length8.007633588
Min length4

Characters and Unicode

Total characters1049
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st rowScripted
2nd rowTalk Show
3rd rowScripted
4th rowScripted
5th rowVariety

Common Values

ValueCountFrequency (%)
Scripted89
67.9%
Reality15
 
11.5%
Talk Show7
 
5.3%
Animation7
 
5.3%
Documentary5
 
3.8%
Variety3
 
2.3%
News2
 
1.5%
Sports2
 
1.5%
Panel Show1
 
0.8%

Length

2022-05-09T21:24:53.166810image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:24:53.276147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
scripted89
64.0%
reality15
 
10.8%
show8
 
5.8%
talk7
 
5.0%
animation7
 
5.0%
documentary5
 
3.6%
variety3
 
2.2%
news2
 
1.4%
sports2
 
1.4%
panel1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
i121
11.5%
t121
11.5%
e115
11.0%
S99
9.4%
r99
9.4%
c94
9.0%
p91
8.7%
d89
8.5%
a38
 
3.6%
l23
 
2.2%
Other values (17)159
15.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter902
86.0%
Uppercase Letter139
 
13.3%
Space Separator8
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i121
13.4%
t121
13.4%
e115
12.7%
r99
11.0%
c94
10.4%
p91
10.1%
d89
9.9%
a38
 
4.2%
l23
 
2.5%
y23
 
2.5%
Other values (8)88
9.8%
Uppercase Letter
ValueCountFrequency (%)
S99
71.2%
R15
 
10.8%
A7
 
5.0%
T7
 
5.0%
D5
 
3.6%
V3
 
2.2%
N2
 
1.4%
P1
 
0.7%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1041
99.2%
Common8
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i121
11.6%
t121
11.6%
e115
11.0%
S99
9.5%
r99
9.5%
c94
9.0%
p91
8.7%
d89
8.5%
a38
 
3.7%
l23
 
2.2%
Other values (16)151
14.5%
Common
ValueCountFrequency (%)
8
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1049
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i121
11.5%
t121
11.5%
e115
11.0%
S99
9.4%
r99
9.4%
c94
9.0%
p91
8.7%
d89
8.5%
a38
 
3.6%
l23
 
2.2%
Other values (17)159
15.2%

_embedded_show_language
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
English
49 
Chinese
38 
Russian
12 
Korean
Turkish
Other values (11)
20 

Length

Max length10
Median length7
Mean length6.870229008
Min length3

Characters and Unicode

Total characters900
Distinct characters33
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)4.6%

Sample

1st rowRussian
2nd rowRussian
3rd rowRussian
4th rowRussian
5th rowRussian

Common Values

ValueCountFrequency (%)
English49
37.4%
Chinese38
29.0%
Russian12
 
9.2%
Korean7
 
5.3%
Turkish5
 
3.8%
Arabic4
 
3.1%
Tagalog3
 
2.3%
French3
 
2.3%
Swedish2
 
1.5%
Dutch2
 
1.5%
Other values (6)6
 
4.6%

Length

2022-05-09T21:24:53.369845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english49
37.4%
chinese38
29.0%
russian12
 
9.2%
korean7
 
5.3%
turkish5
 
3.8%
arabic4
 
3.1%
tagalog3
 
2.3%
french3
 
2.3%
swedish2
 
1.5%
dutch2
 
1.5%
Other values (6)6
 
4.6%

Most occurring characters

ValueCountFrequency (%)
s120
13.3%
n114
12.7%
i112
12.4%
h99
11.0%
e95
10.6%
g57
6.3%
l53
5.9%
E49
 
5.4%
C38
 
4.2%
a35
 
3.9%
Other values (23)128
14.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter770
85.6%
Uppercase Letter130
 
14.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s120
15.6%
n114
14.8%
i112
14.5%
h99
12.9%
e95
12.3%
g57
7.4%
l53
6.9%
a35
 
4.5%
r22
 
2.9%
u21
 
2.7%
Other values (9)42
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
E49
37.7%
C38
29.2%
R12
 
9.2%
T8
 
6.2%
K7
 
5.4%
A4
 
3.1%
F3
 
2.3%
S2
 
1.5%
D2
 
1.5%
M1
 
0.8%
Other values (4)4
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Latin900
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s120
13.3%
n114
12.7%
i112
12.4%
h99
11.0%
e95
10.6%
g57
6.3%
l53
5.9%
E49
 
5.4%
C38
 
4.2%
a35
 
3.9%
Other values (23)128
14.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s120
13.3%
n114
12.7%
i112
12.4%
h99
11.0%
e95
10.6%
g57
6.3%
l53
5.9%
E49
 
5.4%
C38
 
4.2%
a35
 
3.9%
Other values (23)128
14.2%

_embedded_show_genres
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct29
Distinct (%)22.1%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
[]
29 
['Comedy']
20 
['Drama', 'Romance']
12 
['Horror', 'Romance', 'Supernatural']
['Drama', 'Comedy', 'Romance']
Other values (24)
55 

Length

Max length42
Median length38
Mean length17.74045802
Min length2

Characters and Unicode

Total characters2324
Distinct characters32
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)9.9%

Sample

1st row['Drama', 'Crime', 'Mystery']
2nd row[]
3rd row['Drama', 'Comedy']
4th row['Drama', 'Comedy', 'Science-Fiction']
5th row[]

Common Values

ValueCountFrequency (%)
[]29
22.1%
['Comedy']20
15.3%
['Drama', 'Romance']12
9.2%
['Horror', 'Romance', 'Supernatural']8
 
6.1%
['Drama', 'Comedy', 'Romance']7
 
5.3%
['Comedy', 'Fantasy', 'Romance']7
 
5.3%
['Drama', 'Crime', 'Thriller']6
 
4.6%
['Mystery']6
 
4.6%
['Sports']4
 
3.1%
['Drama', 'Romance', 'History']4
 
3.1%
Other values (19)28
21.4%

Length

2022-05-09T21:24:53.480395image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
drama47
19.1%
comedy44
17.9%
romance42
17.1%
29
11.8%
crime12
 
4.9%
horror9
 
3.7%
supernatural9
 
3.7%
fantasy9
 
3.7%
mystery8
 
3.3%
action7
 
2.8%
Other values (10)30
12.2%

Most occurring characters

ValueCountFrequency (%)
'434
18.7%
a173
 
7.4%
m149
 
6.4%
e138
 
5.9%
r138
 
5.9%
[131
 
5.6%
]131
 
5.6%
o123
 
5.3%
,115
 
4.9%
115
 
4.9%
Other values (22)677
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1177
50.6%
Other Punctuation549
23.6%
Uppercase Letter219
 
9.4%
Open Punctuation131
 
5.6%
Close Punctuation131
 
5.6%
Space Separator115
 
4.9%
Dash Punctuation2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a173
14.7%
m149
12.7%
e138
11.7%
r138
11.7%
o123
10.5%
n80
6.8%
y74
6.3%
c58
 
4.9%
d51
 
4.3%
t47
 
4.0%
Other values (7)146
12.4%
Uppercase Letter
ValueCountFrequency (%)
C58
26.5%
D47
21.5%
R42
19.2%
S15
 
6.8%
A14
 
6.4%
F13
 
5.9%
H13
 
5.9%
M11
 
5.0%
T6
 
2.7%
Other Punctuation
ValueCountFrequency (%)
'434
79.1%
,115
 
20.9%
Open Punctuation
ValueCountFrequency (%)
[131
100.0%
Close Punctuation
ValueCountFrequency (%)
]131
100.0%
Space Separator
ValueCountFrequency (%)
115
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1396
60.1%
Common928
39.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a173
12.4%
m149
10.7%
e138
 
9.9%
r138
 
9.9%
o123
 
8.8%
n80
 
5.7%
y74
 
5.3%
c58
 
4.2%
C58
 
4.2%
d51
 
3.7%
Other values (16)354
25.4%
Common
ValueCountFrequency (%)
'434
46.8%
[131
 
14.1%
]131
 
14.1%
,115
 
12.4%
115
 
12.4%
-2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2324
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
'434
18.7%
a173
 
7.4%
m149
 
6.4%
e138
 
5.9%
r138
 
5.9%
[131
 
5.6%
]131
 
5.6%
o123
 
5.3%
,115
 
4.9%
115
 
4.9%
Other values (22)677
29.1%

_embedded_show_status
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Running
69 
Ended
49 
To Be Determined
13 

Length

Max length16
Median length7
Mean length7.145038168
Min length5

Characters and Unicode

Total characters936
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEnded
2nd rowRunning
3rd rowEnded
4th rowTo Be Determined
5th rowEnded

Common Values

ValueCountFrequency (%)
Running69
52.7%
Ended49
37.4%
To Be Determined13
 
9.9%

Length

2022-05-09T21:24:53.589894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:24:53.683630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
running69
43.9%
ended49
31.2%
to13
 
8.3%
be13
 
8.3%
determined13
 
8.3%

Most occurring characters

ValueCountFrequency (%)
n269
28.7%
d111
11.9%
e101
 
10.8%
i82
 
8.8%
R69
 
7.4%
u69
 
7.4%
g69
 
7.4%
E49
 
5.2%
26
 
2.8%
T13
 
1.4%
Other values (6)78
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter753
80.4%
Uppercase Letter157
 
16.8%
Space Separator26
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n269
35.7%
d111
14.7%
e101
 
13.4%
i82
 
10.9%
u69
 
9.2%
g69
 
9.2%
o13
 
1.7%
t13
 
1.7%
r13
 
1.7%
m13
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
R69
43.9%
E49
31.2%
T13
 
8.3%
B13
 
8.3%
D13
 
8.3%
Space Separator
ValueCountFrequency (%)
26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin910
97.2%
Common26
 
2.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n269
29.6%
d111
12.2%
e101
 
11.1%
i82
 
9.0%
R69
 
7.6%
u69
 
7.6%
g69
 
7.6%
E49
 
5.4%
T13
 
1.4%
o13
 
1.4%
Other values (5)65
 
7.1%
Common
ValueCountFrequency (%)
26
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII936
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n269
28.7%
d111
11.9%
e101
 
10.8%
i82
 
8.8%
R69
 
7.4%
u69
 
7.4%
g69
 
7.4%
E49
 
5.2%
26
 
2.8%
T13
 
1.4%
Other values (6)78
 
8.3%

_embedded_show_runtime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct19
Distinct (%)21.3%
Missing42
Missing (%)32.1%
Infinite0
Infinite (%)0.0%
Mean40.30337079
Minimum4
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-09T21:24:53.761875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile9.4
Q130
median45
Q345
95-th percentile60
Maximum120
Range116
Interquartile range (IQR)15

Descriptive statistics

Standard deviation21.29468804
Coefficient of variation (CV)0.5283599763
Kurtosis4.854048443
Mean40.30337079
Median Absolute Deviation (MAD)15
Skewness1.473779527
Sum3587
Variance453.4637385
MonotonicityNot monotonic
2022-05-09T21:24:53.840782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
4526
19.8%
6012
 
9.2%
3012
 
9.2%
208
 
6.1%
386
 
4.6%
103
 
2.3%
513
 
2.3%
1203
 
2.3%
502
 
1.5%
252
 
1.5%
Other values (9)12
 
9.2%
(Missing)42
32.1%
ValueCountFrequency (%)
42
 
1.5%
51
 
0.8%
81
 
0.8%
91
 
0.8%
103
 
2.3%
208
6.1%
212
 
1.5%
231
 
0.8%
252
 
1.5%
281
 
0.8%
ValueCountFrequency (%)
1203
 
2.3%
6012
9.2%
531
 
0.8%
513
 
2.3%
502
 
1.5%
4526
19.8%
402
 
1.5%
386
 
4.6%
3012
9.2%
281
 
0.8%

_embedded_show_averageRuntime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct33
Distinct (%)27.7%
Missing12
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean39.2605042
Minimum2
Maximum129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-09T21:24:54.062447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7.9
Q128
median42
Q351
95-th percentile60
Maximum129
Range127
Interquartile range (IQR)23

Descriptive statistics

Standard deviation21.02438543
Coefficient of variation (CV)0.5355098173
Kurtosis4.662717442
Mean39.2605042
Median Absolute Deviation (MAD)12
Skewness1.300643161
Sum4672
Variance442.0247828
MonotonicityNot monotonic
2022-05-09T21:24:54.151924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
4523
17.6%
3021
16.0%
6011
 
8.4%
5710
 
7.6%
426
 
4.6%
215
 
3.8%
104
 
3.1%
283
 
2.3%
523
 
2.3%
72
 
1.5%
Other values (23)31
23.7%
(Missing)12
 
9.2%
ValueCountFrequency (%)
21
 
0.8%
42
1.5%
51
 
0.8%
72
1.5%
81
 
0.8%
92
1.5%
104
3.1%
121
 
0.8%
161
 
0.8%
202
1.5%
ValueCountFrequency (%)
1291
 
0.8%
1202
 
1.5%
6011
8.4%
581
 
0.8%
5710
7.6%
561
 
0.8%
531
 
0.8%
523
 
2.3%
502
 
1.5%
481
 
0.8%

_embedded_show_premiered
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)42.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2020-12-31
14 
2017-10-17
11 
2018-10-26
 
8
2020-12-24
 
8
2020-12-30
 
7
Other values (51)
83 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1310
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)26.7%

Sample

1st row2018-10-29
2nd row2015-02-13
3rd row2020-03-26
4th row2020-03-26
5th row2020-10-13

Common Values

ValueCountFrequency (%)
2020-12-3114
 
10.7%
2017-10-1711
 
8.4%
2018-10-268
 
6.1%
2020-12-248
 
6.1%
2020-12-307
 
5.3%
2018-11-136
 
4.6%
2019-01-176
 
4.6%
2020-12-175
 
3.8%
2020-12-105
 
3.8%
2020-12-283
 
2.3%
Other values (46)58
44.3%

Length

2022-05-09T21:24:54.261821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-3114
 
10.7%
2017-10-1711
 
8.4%
2018-10-268
 
6.1%
2020-12-248
 
6.1%
2020-12-307
 
5.3%
2018-11-136
 
4.6%
2019-01-176
 
4.6%
2020-12-175
 
3.8%
2020-12-105
 
3.8%
2020-12-283
 
2.3%
Other values (46)58
44.3%

Most occurring characters

ValueCountFrequency (%)
2311
23.7%
0301
23.0%
-262
20.0%
1254
19.4%
343
 
3.3%
739
 
3.0%
830
 
2.3%
623
 
1.8%
921
 
1.6%
416
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1048
80.0%
Dash Punctuation262
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2311
29.7%
0301
28.7%
1254
24.2%
343
 
4.1%
739
 
3.7%
830
 
2.9%
623
 
2.2%
921
 
2.0%
416
 
1.5%
510
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
-262
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2311
23.7%
0301
23.0%
-262
20.0%
1254
19.4%
343
 
3.3%
739
 
3.0%
830
 
2.3%
623
 
1.8%
921
 
1.6%
416
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII1310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2311
23.7%
0301
23.0%
-262
20.0%
1254
19.4%
343
 
3.3%
739
 
3.0%
830
 
2.3%
623
 
1.8%
921
 
1.6%
416
 
1.2%

_embedded_show_ended
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct18
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
nan
82 
2021-01-07
13 
2020-12-31
12 
2021-01-14
 
4
2021-01-02
 
2
Other values (13)
18 

Length

Max length10
Median length3
Mean length5.618320611
Min length3

Characters and Unicode

Total characters736
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)6.1%

Sample

1st row2021-01-07
2nd rownan
3rd row2021-01-21
4th rownan
5th row2020-12-31

Common Values

ValueCountFrequency (%)
nan82
62.6%
2021-01-0713
 
9.9%
2020-12-3112
 
9.2%
2021-01-144
 
3.1%
2021-01-022
 
1.5%
2021-10-072
 
1.5%
2020-12-182
 
1.5%
2021-01-282
 
1.5%
2021-01-042
 
1.5%
2021-01-112
 
1.5%
Other values (8)8
 
6.1%

Length

2022-05-09T21:24:54.340435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan82
62.6%
2021-01-0713
 
9.9%
2020-12-3112
 
9.2%
2021-01-144
 
3.1%
2021-01-282
 
1.5%
2021-01-042
 
1.5%
2021-01-112
 
1.5%
2020-12-182
 
1.5%
2021-10-072
 
1.5%
2021-01-022
 
1.5%
Other values (8)8
 
6.1%

Most occurring characters

ValueCountFrequency (%)
n164
22.3%
2124
16.8%
0120
16.3%
1107
14.5%
-98
13.3%
a82
11.1%
716
 
2.2%
312
 
1.6%
47
 
1.0%
84
 
0.5%
Other values (2)2
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number392
53.3%
Lowercase Letter246
33.4%
Dash Punctuation98
 
13.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2124
31.6%
0120
30.6%
1107
27.3%
716
 
4.1%
312
 
3.1%
47
 
1.8%
84
 
1.0%
61
 
0.3%
51
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
n164
66.7%
a82
33.3%
Dash Punctuation
ValueCountFrequency (%)
-98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common490
66.6%
Latin246
33.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2124
25.3%
0120
24.5%
1107
21.8%
-98
20.0%
716
 
3.3%
312
 
2.4%
47
 
1.4%
84
 
0.8%
61
 
0.2%
51
 
0.2%
Latin
ValueCountFrequency (%)
n164
66.7%
a82
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII736
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n164
22.3%
2124
16.8%
0120
16.3%
1107
14.5%
-98
13.3%
a82
11.1%
716
 
2.2%
312
 
1.6%
47
 
1.0%
84
 
0.5%
Other values (2)2
 
0.3%

_embedded_show_officialSite
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct62
Distinct (%)47.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
nan
28 
https://www.discoveryplus.co.uk/show/three-men-four-wheels
10 
https://www.netflix.com/title/80223989
http://www.iqiyi.com/a_19rrhvpyyp.html
 
6
https://v.qq.com/x/cover/mzc00200tyfmlws.html
 
6
Other values (57)
73 

Length

Max length250
Median length72
Mean length40.80152672
Min length3

Characters and Unicode

Total characters5345
Distinct characters74
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)35.1%

Sample

1st rowhttps://premier.one/show/8405
2nd rowhttp://seasonvar.ru/serial-11488-Po_sezonu_Videodajdzhest_Seasonvar.html
3rd rowhttps://start.ru/watch/257-prichin-chtoby-zhit
4th rowhttps://hd.kinopoisk.ru/film/48ec62883cb1bfc8a65154fcd3749b72
5th rowhttps://premier.one/show/13734

Common Values

ValueCountFrequency (%)
nan28
21.4%
https://www.discoveryplus.co.uk/show/three-men-four-wheels10
 
7.6%
https://www.netflix.com/title/802239898
 
6.1%
http://www.iqiyi.com/a_19rrhvpyyp.html6
 
4.6%
https://v.qq.com/x/cover/mzc00200tyfmlws.html6
 
4.6%
https://www.bet.plus/shows/the-family-business6
 
4.6%
https://www.zed.fr/fr/tv/distribution/catalogue/programme/forteresses-assiegees-batailles-de-legende3
 
2.3%
https://www.wavve.com/player/vod?programid=C9901_C99000000047&page=12
 
1.5%
https://www.ivi.ru/watch/chuma-20202
 
1.5%
https://www.tytnetwork.com2
 
1.5%
Other values (52)58
44.3%

Length

2022-05-09T21:24:54.462826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nan28
21.4%
https://www.discoveryplus.co.uk/show/three-men-four-wheels10
 
7.6%
https://www.netflix.com/title/802239898
 
6.1%
http://www.iqiyi.com/a_19rrhvpyyp.html6
 
4.6%
https://v.qq.com/x/cover/mzc00200tyfmlws.html6
 
4.6%
https://www.bet.plus/shows/the-family-business6
 
4.6%
https://www.zed.fr/fr/tv/distribution/catalogue/programme/forteresses-assiegees-batailles-de-legende3
 
2.3%
https://www.iqiyi.com/a_nvzsmw0tgx.html2
 
1.5%
https://v.qq.com/detail/s/sifd2an7kx2h9h8.html2
 
1.5%
https://www.iqiyi.com/lib/m_213579814.html2
 
1.5%
Other values (52)58
44.3%

Most occurring characters

ValueCountFrequency (%)
/428
 
8.0%
t403
 
7.5%
s311
 
5.8%
w289
 
5.4%
e276
 
5.2%
.233
 
4.4%
h223
 
4.2%
o213
 
4.0%
i205
 
3.8%
l189
 
3.5%
Other values (64)2575
48.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3703
69.3%
Other Punctuation822
 
15.4%
Decimal Number489
 
9.1%
Uppercase Letter188
 
3.5%
Dash Punctuation97
 
1.8%
Connector Punctuation26
 
0.5%
Math Symbol20
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t403
 
10.9%
s311
 
8.4%
w289
 
7.8%
e276
 
7.5%
h223
 
6.0%
o213
 
5.8%
i205
 
5.5%
l189
 
5.1%
p179
 
4.8%
a161
 
4.3%
Other values (16)1254
33.9%
Uppercase Letter
ValueCountFrequency (%)
P23
 
12.2%
L16
 
8.5%
C14
 
7.4%
B14
 
7.4%
D10
 
5.3%
E10
 
5.3%
A10
 
5.3%
M9
 
4.8%
Z9
 
4.8%
F8
 
4.3%
Other values (15)65
34.6%
Decimal Number
ValueCountFrequency (%)
075
15.3%
163
12.9%
259
12.1%
957
11.7%
853
10.8%
343
8.8%
437
7.6%
537
7.6%
735
7.2%
630
 
6.1%
Other Punctuation
ValueCountFrequency (%)
/428
52.1%
.233
28.3%
:124
 
15.1%
%15
 
1.8%
?13
 
1.6%
&5
 
0.6%
'2
 
0.2%
#1
 
0.1%
!1
 
0.1%
Math Symbol
ValueCountFrequency (%)
=18
90.0%
+2
 
10.0%
Dash Punctuation
ValueCountFrequency (%)
-97
100.0%
Connector Punctuation
ValueCountFrequency (%)
_26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3891
72.8%
Common1454
 
27.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t403
 
10.4%
s311
 
8.0%
w289
 
7.4%
e276
 
7.1%
h223
 
5.7%
o213
 
5.5%
i205
 
5.3%
l189
 
4.9%
p179
 
4.6%
a161
 
4.1%
Other values (41)1442
37.1%
Common
ValueCountFrequency (%)
/428
29.4%
.233
16.0%
:124
 
8.5%
-97
 
6.7%
075
 
5.2%
163
 
4.3%
259
 
4.1%
957
 
3.9%
853
 
3.6%
343
 
3.0%
Other values (13)222
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/428
 
8.0%
t403
 
7.5%
s311
 
5.8%
w289
 
5.4%
e276
 
5.2%
.233
 
4.4%
h223
 
4.2%
o213
 
4.0%
i205
 
3.8%
l189
 
3.5%
Other values (64)2575
48.2%

_embedded_show_weight
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct49
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.19083969
Minimum0
Maximum100
Zeros6
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-09T21:24:54.578713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q117
median35
Q376
95-th percentile95
Maximum100
Range100
Interquartile range (IQR)59

Descriptive statistics

Standard deviation33.07686026
Coefficient of variation (CV)0.7319372794
Kurtosis-1.363699923
Mean45.19083969
Median Absolute Deviation (MAD)25
Skewness0.3194428996
Sum5920
Variance1094.078685
MonotonicityNot monotonic
2022-05-09T21:24:54.688906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
9317
 
13.0%
3511
 
8.4%
958
 
6.1%
177
 
5.3%
686
 
4.6%
146
 
4.6%
06
 
4.6%
84
 
3.1%
184
 
3.1%
14
 
3.1%
Other values (39)58
44.3%
ValueCountFrequency (%)
06
4.6%
14
3.1%
21
 
0.8%
31
 
0.8%
41
 
0.8%
61
 
0.8%
71
 
0.8%
84
3.1%
101
 
0.8%
111
 
0.8%
ValueCountFrequency (%)
1001
 
0.8%
958
6.1%
941
 
0.8%
9317
13.0%
901
 
0.8%
871
 
0.8%
842
 
1.5%
781
 
0.8%
771
 
0.8%
751
 
0.8%

_embedded_show_dvdCountry
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
nan
129 
{'name': 'Korea, Republic of', 'code': 'KR', 'timezone': 'Asia/Seoul'}
 
2

Length

Max length70
Median length3
Mean length4.022900763
Min length3

Characters and Unicode

Total characters527
Distinct characters28
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownan
2nd rownan
3rd rownan
4th rownan
5th rownan

Common Values

ValueCountFrequency (%)
nan129
98.5%
{'name': 'Korea, Republic of', 'code': 'KR', 'timezone': 'Asia/Seoul'}2
 
1.5%

Length

2022-05-09T21:24:54.798717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-05-09T21:24:54.896548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
nan129
89.0%
name2
 
1.4%
korea2
 
1.4%
republic2
 
1.4%
of2
 
1.4%
code2
 
1.4%
kr2
 
1.4%
timezone2
 
1.4%
asia/seoul2
 
1.4%

Most occurring characters

ValueCountFrequency (%)
n262
49.7%
a135
25.6%
'24
 
4.6%
e14
 
2.7%
14
 
2.7%
o10
 
1.9%
i6
 
1.1%
:6
 
1.1%
,6
 
1.1%
R4
 
0.8%
Other values (18)46
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter459
87.1%
Other Punctuation38
 
7.2%
Space Separator14
 
2.7%
Uppercase Letter12
 
2.3%
Open Punctuation2
 
0.4%
Close Punctuation2
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n262
57.1%
a135
29.4%
e14
 
3.1%
o10
 
2.2%
i6
 
1.3%
c4
 
0.9%
l4
 
0.9%
u4
 
0.9%
m4
 
0.9%
p2
 
0.4%
Other values (7)14
 
3.1%
Other Punctuation
ValueCountFrequency (%)
'24
63.2%
:6
 
15.8%
,6
 
15.8%
/2
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
R4
33.3%
K4
33.3%
A2
16.7%
S2
16.7%
Space Separator
ValueCountFrequency (%)
14
100.0%
Open Punctuation
ValueCountFrequency (%)
{2
100.0%
Close Punctuation
ValueCountFrequency (%)
}2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin471
89.4%
Common56
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n262
55.6%
a135
28.7%
e14
 
3.0%
o10
 
2.1%
i6
 
1.3%
R4
 
0.8%
c4
 
0.8%
l4
 
0.8%
u4
 
0.8%
K4
 
0.8%
Other values (11)24
 
5.1%
Common
ValueCountFrequency (%)
'24
42.9%
14
25.0%
:6
 
10.7%
,6
 
10.7%
{2
 
3.6%
/2
 
3.6%
}2
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII527
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n262
49.7%
a135
25.6%
'24
 
4.6%
e14
 
2.7%
14
 
2.7%
o10
 
1.9%
i6
 
1.1%
:6
 
1.1%
,6
 
1.1%
R4
 
0.8%
Other values (18)46
 
8.7%

_embedded_show_summary
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct61
Distinct (%)46.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
nan
11 
<p>This series centers on <b>Loudermilk </b>who is a recovering alcoholic and substance-abuse counselor with an extremely bad attitude about everything. He is unapologetically uncensored and has managed to piss off everyone. Although he has his drinking under control, Loudermilk's life is one step forward and 12 steps backwards.</p>
10 
<p>Antique and classic car dealer Drew Pritchard, motoring broadcaster Andy Jaye and racing driver Marino Franchitti set out to discover the greatest racing cars of all time.</p>
10 
<p><b>Chilling Adventures of Sabrina</b> reimagines the origin &amp; adventures of Sabrina the Teenage Witch as a dark coming-of-age story that traffics in horror, the occult and, of course, witchcraft. This adaptation finds Sabrina wrestling to reconcile her dual nature —half-witch, half-mortal — while standing against the evil forces that threaten her, her family and the daylight world humans inhabit.</p>
 
8
<p>Lin Luojing enters the XR system due to a technology competition, and time-travels to the Sheng Yuan Dynasty of the game. To return back to reality, she has to find her true love and max the "favorability points". In the midst of exchanging tactics with arrogant prince Zhong Wu Mei, her former personal guard Liu Xiu Wen returns to the capital, this time with a new identity as the Persian Prince. Liu Xiu Wen vows to wage war on Zhong Wuyan. Facing both internal and external crises and conflicts, how will Lin Luojing resolve it and embark on her journey back home?</p>
 
6
Other values (56)
86 

Length

Max length1261
Median length729
Mean length417.0687023
Min length3

Characters and Unicode

Total characters54636
Distinct characters87
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)29.8%

Sample

1st row<p>Marina is in her late 30s, she has a successful business and a close-knit family. Her husband is a surgeon and her daughters study at fancy establishments. To everybody her life seems perfect. Though, it is all just a facade concealing the real problems: her husband has a mistress, her elder daughter is a slacker and drug-dealer, her youngest is a sociopath. Well, Marina herself is not really a flower-lady, but a brothel-keeper who is hiding her dark business from everyone. The truth may come out when a girl of Marina's is found dead.</p>
2nd row<p>Weekly videodaydzhest on site seasonvar.ru and creative team viruseproject.tv. In ten minutes, we talk about the most important events of the past week: look down on the set is not yet published projects, sharing the secrets of private life actors consider the prospects for the development of genres and discuss news TV industry! In videodaydzheste you will find only reliable information from Russian and foreign publications, as well as take part in choosing the best show of the month! Our weekly news videodaydzhest will suit every viewer, so gather good company with family and friends, as well as stock up on popcorn - these ten minutes you shock, delight and inform the latest news about your favorite TV projects!</p>
3rd row<p>When terminal cancer patient Zhenya unexpectedly receives a clean bill of health, she can't believe it. She's in remission. But then her life implodes. Homeless, unemployed, and newly single - she stumbles across a list she wrote while she was sick of things she wanted to do when she got better. 257 of them - and now she won't give up until she checks off them all!</p>
4th rownan
5th row<p>Solo performances of stand-up comedians from the underground and popular TV and Internet projects. Each new release is a new concert with its own atmosphere and humor.</p>

Common Values

ValueCountFrequency (%)
nan11
 
8.4%
<p>This series centers on <b>Loudermilk </b>who is a recovering alcoholic and substance-abuse counselor with an extremely bad attitude about everything. He is unapologetically uncensored and has managed to piss off everyone. Although he has his drinking under control, Loudermilk's life is one step forward and 12 steps backwards.</p>10
 
7.6%
<p>Antique and classic car dealer Drew Pritchard, motoring broadcaster Andy Jaye and racing driver Marino Franchitti set out to discover the greatest racing cars of all time.</p>10
 
7.6%
<p><b>Chilling Adventures of Sabrina</b> reimagines the origin &amp; adventures of Sabrina the Teenage Witch as a dark coming-of-age story that traffics in horror, the occult and, of course, witchcraft. This adaptation finds Sabrina wrestling to reconcile her dual nature —half-witch, half-mortal — while standing against the evil forces that threaten her, her family and the daylight world humans inhabit.</p>8
 
6.1%
<p>Lin Luojing enters the XR system due to a technology competition, and time-travels to the Sheng Yuan Dynasty of the game. To return back to reality, she has to find her true love and max the "favorability points". In the midst of exchanging tactics with arrogant prince Zhong Wu Mei, her former personal guard Liu Xiu Wen returns to the capital, this time with a new identity as the Persian Prince. Liu Xiu Wen vows to wage war on Zhong Wuyan. Facing both internal and external crises and conflicts, how will Lin Luojing resolve it and embark on her journey back home?</p>6
 
4.6%
<p>Young noble Chu Yun Xiao crosses paths with female doctor Leng Xing Chen because of a beauty portrait. Together with their friends, the six people who are pulled into a terrifying conspiracy form a detective team to uncover the secrets surrounding the portrait. Chu Yunxiao has ventured into the pugilistic world for the first time. Aspiring to be a chivalrous hero, he relies on his outstanding martial arts skills and high intelligence to solve a difficult case. However, he unexpectedly discovers that he is also a chess piece in this dangerous game. As the fog is slowly lifted, Chu Yun Xiao becomes aware of an unbearable truth that the past twenty years of his life was nothing but a lie.</p>6
 
4.6%
<p>Meet the Duncans, a prominent family from Jamaica, Queens living fast and luxurious. By day, they're an upstanding family; by night, they live a dangerous secret life. The patriarch of the family, L.C. Duncan, is at the age when he's starting to think about retirement and has to decide which of his children should take over his thriving exotic car dealership. The Duncans quickly come under siege from some of the top politicians, mafia and drug cartels in the city. The Duncans will have to stick together or die separately.</p>6
 
4.6%
<p>Lin Luo Jing accidentally gets drawn into a game world where she is the daughter of the prime minister and meets all kind of beautiful men with different personalities. Among them are a sword deity, an imperial bodyguard, a playful rich man and an arrogant prince. The system informs her that she can only return to the real world after she finds her true love. While there seems tobe an abundance of good men around Luo Jing, there is one man she can't stand at all: the prince of the barbarian Yuan Kingdom Zhong Wu Mei. But out of all men, she ends up in an arranged marriage with Wu Mei.</p><p>Thus begins their love-hate relationship and her journey to find true love in order to win the game.</p>6
 
4.6%
<p>Built at strategic points, and fitted with impressive fortifications and ingenious systems to counter attacks, fortresses are thought to be impenetrable. And yet, certain skillful warlords have successfully stormed them. How did they manage this?</p><p>By recounting how some of the most remarkable sieges - in ancient times or in medieval history - played out, this series revisits the construction of these megastructures and reveals the different strategies used to lay or to endure a siege. Thanks to CGI, dramatized scenes, and with the help of key experts, it immerses us in the compelling confrontation between the construction genius of the military strategists and the ingenuity of some exceptional warriors.</p>3
 
2.3%
<p>The story happens at the end of the Tang Dynasty, when Zhu Wen usurps the throne and establishes the Later Liang Dynasty, and he's known as Emperor Taizu. Ma Zhai Xing (Li Qin) is the daughter of an official and as a child, she befriends a young boy (Darren Wang) who lives in the mountain. One day when he saves a wolf, he accidentally falls over the cliff and is rescued by Zhu Wen. The authoritative figure adopts him as a godson and gives him the title Bo Wang. Ten years later, he saves the female lead per chance and finds her courage and intelligence resonant, and she likes that while he's in a position of power, he still has humility and kindness. She encourages him to fight for justice and he begins that journey by helping the people, stopping throne fights, etc. Even when they have conflicts, they will face those frankly. As they overcome obstacles and fight for justice, feelings deepen and they are able to reap their own happiness by each other's side.</p>2
 
1.5%
Other values (51)63
48.1%

Length

2022-05-09T21:24:55.006180image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the525
 
5.8%
and322
 
3.5%
of259
 
2.8%
to251
 
2.8%
a244
 
2.7%
in149
 
1.6%
is132
 
1.4%
her119
 
1.3%
with100
 
1.1%
his82
 
0.9%
Other values (1772)6926
76.0%

Most occurring characters

ValueCountFrequency (%)
8970
16.4%
e5017
 
9.2%
t3539
 
6.5%
a3504
 
6.4%
n3264
 
6.0%
i3196
 
5.8%
o2945
 
5.4%
r2778
 
5.1%
s2671
 
4.9%
h2326
 
4.3%
Other values (77)16426
30.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter41712
76.3%
Space Separator8978
 
16.4%
Uppercase Letter1617
 
3.0%
Other Punctuation1339
 
2.5%
Math Symbol764
 
1.4%
Dash Punctuation125
 
0.2%
Decimal Number69
 
0.1%
Format14
 
< 0.1%
Close Punctuation9
 
< 0.1%
Open Punctuation9
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e5017
12.0%
t3539
 
8.5%
a3504
 
8.4%
n3264
 
7.8%
i3196
 
7.7%
o2945
 
7.1%
r2778
 
6.7%
s2671
 
6.4%
h2326
 
5.6%
l1711
 
4.1%
Other values (20)10761
25.8%
Uppercase Letter
ValueCountFrequency (%)
T182
 
11.3%
S158
 
9.8%
A116
 
7.2%
W115
 
7.1%
L113
 
7.0%
Y97
 
6.0%
X95
 
5.9%
C80
 
4.9%
H74
 
4.6%
M72
 
4.5%
Other values (16)515
31.8%
Other Punctuation
ValueCountFrequency (%)
,504
37.6%
.436
32.6%
/199
 
14.9%
'78
 
5.8%
"32
 
2.4%
;23
 
1.7%
?23
 
1.7%
!18
 
1.3%
:16
 
1.2%
&9
 
0.7%
Decimal Number
ValueCountFrequency (%)
118
26.1%
217
24.6%
015
21.7%
95
 
7.2%
54
 
5.8%
63
 
4.3%
32
 
2.9%
72
 
2.9%
42
 
2.9%
81
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
-98
78.4%
25
 
20.0%
2
 
1.6%
Space Separator
ValueCountFrequency (%)
8970
99.9%
 8
 
0.1%
Math Symbol
ValueCountFrequency (%)
<382
50.0%
>382
50.0%
Format
ValueCountFrequency (%)
14
100.0%
Close Punctuation
ValueCountFrequency (%)
)9
100.0%
Open Punctuation
ValueCountFrequency (%)
(9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin43329
79.3%
Common11307
 
20.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e5017
11.6%
t3539
 
8.2%
a3504
 
8.1%
n3264
 
7.5%
i3196
 
7.4%
o2945
 
6.8%
r2778
 
6.4%
s2671
 
6.2%
h2326
 
5.4%
l1711
 
3.9%
Other values (46)12378
28.6%
Common
ValueCountFrequency (%)
8970
79.3%
,504
 
4.5%
.436
 
3.9%
<382
 
3.4%
>382
 
3.4%
/199
 
1.8%
-98
 
0.9%
'78
 
0.7%
"32
 
0.3%
25
 
0.2%
Other values (21)201
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII54583
99.9%
Punctuation41
 
0.1%
None12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8970
16.4%
e5017
 
9.2%
t3539
 
6.5%
a3504
 
6.4%
n3264
 
6.0%
i3196
 
5.9%
o2945
 
5.4%
r2778
 
5.1%
s2671
 
4.9%
h2326
 
4.3%
Other values (69)16373
30.0%
Punctuation
ValueCountFrequency (%)
25
61.0%
14
34.1%
2
 
4.9%
None
ValueCountFrequency (%)
 8
66.7%
ê1
 
8.3%
ã1
 
8.3%
ö1
 
8.3%
å1
 
8.3%

_embedded_show_updated
Real number (ℝ≥0)

HIGH CORRELATION

Distinct71
Distinct (%)54.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1634629291
Minimum1607964656
Maximum1652016543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2022-05-09T21:24:55.149707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1607964656
5-th percentile1609651676
Q11614901805
median1639070620
Q31649692446
95-th percentile1651863266
Maximum1652016543
Range44051887
Interquartile range (IQR)34790641.5

Descriptive statistics

Standard deviation16257932.35
Coefficient of variation (CV)0.009945944586
Kurtosis-1.382919892
Mean1634629291
Median Absolute Deviation (MAD)11552031
Skewness-0.5222516509
Sum2.141364371 × 1011
Variance2.643203642 × 1014
MonotonicityNot monotonic
2022-05-09T21:24:55.272341image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
163345356910
 
7.6%
161113212510
 
7.6%
16514853588
 
6.1%
16518632666
 
4.6%
16506226516
 
4.6%
16096516766
 
4.6%
16384118956
 
4.6%
16390706203
 
2.3%
16144618932
 
1.5%
16105066872
 
1.5%
Other values (61)72
55.0%
ValueCountFrequency (%)
16079646561
 
0.8%
16094387771
 
0.8%
16094684032
 
1.5%
16096488851
 
0.8%
16096516766
4.6%
16097998962
 
1.5%
16101108411
 
0.8%
16105066872
 
1.5%
161113212510
7.6%
16115090621
 
0.8%
ValueCountFrequency (%)
16520165431
 
0.8%
16520047591
 
0.8%
16519813431
 
0.8%
16518632666
4.6%
16518386471
 
0.8%
16514853588
6.1%
16514160251
 
0.8%
16511816551
 
0.8%
16508602721
 
0.8%
16507350911
 
0.8%

_links_self_href
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct131
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
https://api.tvmaze.com/episodes/1977902
 
1
https://api.tvmaze.com/episodes/2001669
 
1
https://api.tvmaze.com/episodes/2001667
 
1
https://api.tvmaze.com/episodes/2001666
 
1
https://api.tvmaze.com/episodes/2001665
 
1
Other values (126)
126 

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters5109
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique131 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/1977902
2nd rowhttps://api.tvmaze.com/episodes/2015818
3rd rowhttps://api.tvmaze.com/episodes/1964000
4th rowhttps://api.tvmaze.com/episodes/1995405
5th rowhttps://api.tvmaze.com/episodes/2007760

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
0.8%
https://api.tvmaze.com/episodes/20016691
 
0.8%
https://api.tvmaze.com/episodes/20016671
 
0.8%
https://api.tvmaze.com/episodes/20016661
 
0.8%
https://api.tvmaze.com/episodes/20016651
 
0.8%
https://api.tvmaze.com/episodes/20000731
 
0.8%
https://api.tvmaze.com/episodes/20000721
 
0.8%
https://api.tvmaze.com/episodes/19975381
 
0.8%
https://api.tvmaze.com/episodes/19975371
 
0.8%
https://api.tvmaze.com/episodes/19884051
 
0.8%
Other values (121)121
92.4%

Length

2022-05-09T21:24:55.397762image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/19779021
 
0.8%
https://api.tvmaze.com/episodes/23122251
 
0.8%
https://api.tvmaze.com/episodes/19640001
 
0.8%
https://api.tvmaze.com/episodes/19954051
 
0.8%
https://api.tvmaze.com/episodes/20077601
 
0.8%
https://api.tvmaze.com/episodes/19857891
 
0.8%
https://api.tvmaze.com/episodes/20396221
 
0.8%
https://api.tvmaze.com/episodes/20396231
 
0.8%
https://api.tvmaze.com/episodes/23244271
 
0.8%
https://api.tvmaze.com/episodes/23244281
 
0.8%
Other values (121)121
92.4%

Most occurring characters

ValueCountFrequency (%)
/524
 
10.3%
p393
 
7.7%
s393
 
7.7%
e393
 
7.7%
t393
 
7.7%
o262
 
5.1%
a262
 
5.1%
i262
 
5.1%
.262
 
5.1%
m262
 
5.1%
Other values (16)1703
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3275
64.1%
Other Punctuation917
 
17.9%
Decimal Number917
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p393
12.0%
s393
12.0%
e393
12.0%
t393
12.0%
o262
8.0%
a262
8.0%
i262
8.0%
m262
8.0%
h131
 
4.0%
d131
 
4.0%
Other values (3)393
12.0%
Decimal Number
ValueCountFrequency (%)
2142
15.5%
9141
15.4%
0124
13.5%
1111
12.1%
378
8.5%
674
8.1%
871
7.7%
461
6.7%
760
6.5%
555
 
6.0%
Other Punctuation
ValueCountFrequency (%)
/524
57.1%
.262
28.6%
:131
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin3275
64.1%
Common1834
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/524
28.6%
.262
14.3%
2142
 
7.7%
9141
 
7.7%
:131
 
7.1%
0124
 
6.8%
1111
 
6.1%
378
 
4.3%
674
 
4.0%
871
 
3.9%
Other values (3)176
 
9.6%
Latin
ValueCountFrequency (%)
p393
12.0%
s393
12.0%
e393
12.0%
t393
12.0%
o262
8.0%
a262
8.0%
i262
8.0%
m262
8.0%
h131
 
4.0%
d131
 
4.0%
Other values (3)393
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5109
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/524
 
10.3%
p393
 
7.7%
s393
 
7.7%
e393
 
7.7%
t393
 
7.7%
o262
 
5.1%
a262
 
5.1%
i262
 
5.1%
.262
 
5.1%
m262
 
5.1%
Other values (16)1703
33.3%

Interactions

2022-05-09T21:24:46.648202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:21.537241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:27.854896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:30.265177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:32.695171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:34.978303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:39.201267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:41.192861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:43.909907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:47.828740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:23.358194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:28.957914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:31.366383image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:33.714117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:36.442991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:40.080918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:42.495362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:45.325893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:47.933382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:23.875829image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:29.060068image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:31.577795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:33.817522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:36.736842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:40.182923image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:42.602924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:45.430509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:48.043088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:24.399839image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:29.180210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:31.670012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:33.917205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:37.022220image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:40.272494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:42.705749image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:45.532369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:48.139648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:24.858397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:29.270890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:31.775392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:34.007946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:37.295881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:40.362653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:42.837832image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:45.626134image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:48.811734image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:25.848834image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:29.838463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:32.318466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:34.577667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:38.061393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:40.815394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:43.474418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:46.270624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:48.939222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:26.216850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:29.952467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:32.411145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:34.684840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:38.299425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:40.916377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:43.578379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:46.355936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:49.066323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:26.706982image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:30.053537image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:32.499548image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:34.787158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:38.607961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:41.005725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:43.685735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:46.462935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:49.204764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:27.320061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:30.155396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:32.598539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:34.875933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:38.914686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:41.096728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:43.780031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-05-09T21:24:46.551632image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-05-09T21:24:55.476621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-09T21:24:55.630919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-09T21:24:55.795773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-09T21:24:56.043758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-09T21:24:56.256637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-09T21:24:49.530844image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-09T21:24:50.264979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-09T21:24:50.472217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-09T21:24:50.597565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
01977902https://www.tvmaze.com/episodes/1977902/obycnaa-zensina-2x06-seria-15Серия 152.06.0regular2020-12-3110:002020-12-30T22:00:00+00:0056.0nan39115https://www.tvmaze.com/shows/39115/obycnaa-zensinaОбычная женщинаScriptedRussian['Drama', 'Crime', 'Mystery']Ended50.048.02018-10-292021-01-07https://premier.one/show/840536.0nan<p>Marina is in her late 30s, she has a successful business and a close-knit family. Her husband is a surgeon and her daughters study at fancy establishments. To everybody her life seems perfect. Though, it is all just a facade concealing the real problems: her husband has a mistress, her elder daughter is a slacker and drug-dealer, her youngest is a sociopath. Well, Marina herself is not really a flower-lady, but a brothel-keeper who is hiding her dark business from everyone. The truth may come out when a girl of Marina's is found dead.</p>1.610111e+09https://api.tvmaze.com/episodes/1977902
12015818https://www.tvmaze.com/episodes/2015818/po-sezonu-videodajdzest-seasonvar-6x53-vypusk-307Выпуск 3076.053.0regular2020-12-31nan2020-12-31T00:00:00+00:009.0nan7847https://www.tvmaze.com/shows/7847/po-sezonu-videodajdzest-seasonvarПо сезону. Видеодайджест SeasonvarTalk ShowRussian[]Running9.09.02015-02-13nanhttp://seasonvar.ru/serial-11488-Po_sezonu_Videodajdzhest_Seasonvar.html28.0nan<p>Weekly videodaydzhest on site seasonvar.ru and creative team viruseproject.tv. In ten minutes, we talk about the most important events of the past week: look down on the set is not yet published projects, sharing the secrets of private life actors consider the prospects for the development of genres and discuss news TV industry! In videodaydzheste you will find only reliable information from Russian and foreign publications, as well as take part in choosing the best show of the month! Our weekly news videodaydzhest will suit every viewer, so gather good company with family and friends, as well as stock up on popcorn - these ten minutes you shock, delight and inform the latest news about your favorite TV projects!</p>1.651182e+09https://api.tvmaze.com/episodes/2015818
21964000https://www.tvmaze.com/episodes/1964000/257-pricin-ctoby-zit-2x10-seria-23Серия 232.010.0regular2020-12-31nan2020-12-31T00:00:00+00:0025.0nan43722https://www.tvmaze.com/shows/43722/257-pricin-ctoby-zit257 причин, чтобы житьScriptedRussian['Drama', 'Comedy']Ended25.024.02020-03-262021-01-21https://start.ru/watch/257-prichin-chtoby-zhit38.0nan<p>When terminal cancer patient Zhenya unexpectedly receives a clean bill of health, she can't believe it. She's in remission. But then her life implodes. Homeless, unemployed, and newly single - she stumbles across a list she wrote while she was sick of things she wanted to do when she got better. 257 of them - and now she won't give up until she checks off them all!</p>1.617284e+09https://api.tvmaze.com/episodes/1964000
31995405https://www.tvmaze.com/episodes/1995405/proekt-anna-nikolaevna-s02-special-nacaloНачало2.0NaNsignificant_special2020-12-31nan2020-12-31T00:00:00+00:0067.0nan46688https://www.tvmaze.com/shows/46688/proekt-anna-nikolaevnaПроект "Анна Николаевна"ScriptedRussian['Drama', 'Comedy', 'Science-Fiction']To Be Determined50.050.02020-03-26nanhttps://hd.kinopoisk.ru/film/48ec62883cb1bfc8a65154fcd3749b7240.0nannan1.649796e+09https://api.tvmaze.com/episodes/1995405
42007760https://www.tvmaze.com/episodes/2007760/stand-up-autsajd-1x12-aroslav-kondrasovЯрослав Кондрашов1.012.0regular2020-12-3112:002020-12-31T00:00:00+00:0017.0nan51065https://www.tvmaze.com/shows/51065/stand-up-autsajdStand Up АутсайдVarietyRussian[]Ended40.028.02020-10-132020-12-31https://premier.one/show/137343.0nan<p>Solo performances of stand-up comedians from the underground and popular TV and Internet projects. Each new release is a new concert with its own atmosphere and humor.</p>1.616719e+09https://api.tvmaze.com/episodes/2007760
51985789https://www.tvmaze.com/episodes/1985789/theres-a-pit-in-my-senior-martial-brothers-brain-2x11-episode-11Episode 112.011.0regular2020-12-3111:002020-12-31T03:00:00+00:0020.0nan38031https://www.tvmaze.com/shows/38031/theres-a-pit-in-my-senior-martial-brothers-brainThere's a Pit in My Senior Martial Brother's BrainAnimationChinese['Comedy', 'Action', 'Adventure', 'Anime']Running20.010.02018-04-04nanhttps://www.bilibili.com/bangumi/media/md17632/23.0nannan1.607965e+09https://api.tvmaze.com/episodes/1985789
62039622https://www.tvmaze.com/episodes/2039622/witches-1x05-episode-5Episode 51.05.0regular2020-12-31nan2020-12-31T03:00:00+00:0030.0nan53830https://www.tvmaze.com/shows/53830/witchesWitchesVarietyKorean['Sports']Ended30.030.02020-12-172021-01-14https://www.wavve.com/player/vod?programid=C9901_C99000000047&page=115.0nan<p>Yoon Bo Mi of Apink Comedian Kim Min Kyoung, the former rhythmic gymnast Shin Soo Ji, Cheerleader Park Ki Ryang, Anchorwoman Park Ji Young, and Actress Kang So Yeon are huge fans of baseball. They come together to actually try playing baseball themselves instead of just watching it. Although they all work in different industries, they unite thanks to their mutual love of the sport. Together, they strive to compete against other amateur teams. Will their earnest desire to work together and their ambition to win make all the difference? How will they grow together as a team?</p>1.614462e+09https://api.tvmaze.com/episodes/2039622
72039623https://www.tvmaze.com/episodes/2039623/witches-1x06-episode-6Episode 61.06.0regular2020-12-31nan2020-12-31T03:00:00+00:0030.0nan53830https://www.tvmaze.com/shows/53830/witchesWitchesVarietyKorean['Sports']Ended30.030.02020-12-172021-01-14https://www.wavve.com/player/vod?programid=C9901_C99000000047&page=115.0nan<p>Yoon Bo Mi of Apink Comedian Kim Min Kyoung, the former rhythmic gymnast Shin Soo Ji, Cheerleader Park Ki Ryang, Anchorwoman Park Ji Young, and Actress Kang So Yeon are huge fans of baseball. They come together to actually try playing baseball themselves instead of just watching it. Although they all work in different industries, they unite thanks to their mutual love of the sport. Together, they strive to compete against other amateur teams. Will their earnest desire to work together and their ambition to win make all the difference? How will they grow together as a team?</p>1.614462e+09https://api.tvmaze.com/episodes/2039623
82324427https://www.tvmaze.com/episodes/2324427/unique-lady-2x15-episode-15Episode 152.015.0regular2020-12-3112:002020-12-31T04:00:00+00:0040.0nan41490https://www.tvmaze.com/shows/41490/unique-ladyUnique LadyScriptedChinese['Drama', 'Comedy', 'Romance']Ended38.042.02019-01-172021-01-07http://www.iqiyi.com/a_19rrhvpyyp.html68.0nan<p>Lin Luo Jing accidentally gets drawn into a game world where she is the daughter of the prime minister and meets all kind of beautiful men with different personalities. Among them are a sword deity, an imperial bodyguard, a playful rich man and an arrogant prince. The system informs her that she can only return to the real world after she finds her true love. While there seems tobe an abundance of good men around Luo Jing, there is one man she can't stand at all: the prince of the barbarian Yuan Kingdom Zhong Wu Mei. But out of all men, she ends up in an arranged marriage with Wu Mei.</p><p>Thus begins their love-hate relationship and her journey to find true love in order to win the game.</p>1.651863e+09https://api.tvmaze.com/episodes/2324427
92324428https://www.tvmaze.com/episodes/2324428/unique-lady-2x16-episode-16Episode 162.016.0regular2020-12-3112:002020-12-31T04:00:00+00:0040.0nan41490https://www.tvmaze.com/shows/41490/unique-ladyUnique LadyScriptedChinese['Drama', 'Comedy', 'Romance']Ended38.042.02019-01-172021-01-07http://www.iqiyi.com/a_19rrhvpyyp.html68.0nan<p>Lin Luo Jing accidentally gets drawn into a game world where she is the daughter of the prime minister and meets all kind of beautiful men with different personalities. Among them are a sword deity, an imperial bodyguard, a playful rich man and an arrogant prince. The system informs her that she can only return to the real world after she finds her true love. While there seems tobe an abundance of good men around Luo Jing, there is one man she can't stand at all: the prince of the barbarian Yuan Kingdom Zhong Wu Mei. But out of all men, she ends up in an arranged marriage with Wu Mei.</p><p>Thus begins their love-hate relationship and her journey to find true love in order to win the game.</p>1.651863e+09https://api.tvmaze.com/episodes/2324428

Last rows

idurlnameseasonnumbertypeairdateairtimeairstampruntimesummary_embedded_show_id_embedded_show_url_embedded_show_name_embedded_show_type_embedded_show_language_embedded_show_genres_embedded_show_status_embedded_show_runtime_embedded_show_averageRuntime_embedded_show_premiered_embedded_show_ended_embedded_show_officialSite_embedded_show_weight_embedded_show_dvdCountry_embedded_show_summary_embedded_show_updated_links_self_href
1212234297https://www.tvmaze.com/episodes/2234297/forteresses-assiegees-batailles-de-legende-1x03-the-siege-of-orleans-joans-fortressThe Siege of Orléans, Joan's Fortress1.03.0regular2020-12-31nan2020-12-31T17:00:00+00:0051.0<p>It is the most striking event in the Hundred Year War. Between 1428 and 1429, Orleans was subject to a violent and pitiless siege. Hidden away in the extraordinary walled enclosure, for 7 months, the French resisted an English army that was bent on conquering this strategic city on the Loire River.</p>59380https://www.tvmaze.com/shows/59380/forteresses-assiegees-batailles-de-legendeForteresses assiégées, batailles de légendeDocumentaryFrench[]Running51.052.02020-12-31nanhttps://www.zed.fr/fr/tv/distribution/catalogue/programme/forteresses-assiegees-batailles-de-legende1.0nan<p>Built at strategic points, and fitted with impressive fortifications and ingenious systems to counter attacks, fortresses are thought to be impenetrable. And yet, certain skillful warlords have successfully stormed them. How did they manage this?</p><p>By recounting how some of the most remarkable sieges - in ancient times or in medieval history - played out, this series revisits the construction of these megastructures and reveals the different strategies used to lay or to endure a siege. Thanks to CGI, dramatized scenes, and with the help of key experts, it immerses us in the compelling confrontation between the construction genius of the military strategists and the ingenuity of some exceptional warriors.</p>1.639071e+09https://api.tvmaze.com/episodes/2234297
1222236494https://www.tvmaze.com/episodes/2236494/notruf-hafenkante-15x13-nur-ein-atemzugNur ein Atemzug15.013.0regular2020-12-3119:252020-12-31T18:25:00+00:0045.0nan17046https://www.tvmaze.com/shows/17046/notruf-hafenkanteNotruf HafenkanteScriptedGerman['Drama', 'Crime']Running45.050.02007-01-04nanhttps://www.zdf.de/serien/notruf-hafenkante4.0nannan1.645352e+09https://api.tvmaze.com/episodes/2236494
1231977423https://www.tvmaze.com/episodes/1977423/goede-tijden-slechte-tijden-31x72-aflevering-6327Aflevering 632731.072.0regular2020-12-3120:002020-12-31T19:00:00+00:0023.0nan2504https://www.tvmaze.com/shows/2504/goede-tijden-slechte-tijdenGoede Tijden, Slechte TijdenScriptedDutch['Drama', 'Romance']Running23.025.01990-10-01nanhttp://gtst.nl/#!/77.0nannan1.651839e+09https://api.tvmaze.com/episodes/1977423
1241976649https://www.tvmaze.com/episodes/1976649/wwe-nxt-uk-2020-12-31-episode-53Episode 532020.053.0regular2020-12-3115:002020-12-31T20:00:00+00:0060.0nan39053https://www.tvmaze.com/shows/39053/wwe-nxt-ukWWE NXT UKSportsEnglish[]Running60.060.02018-10-17nannan84.0nan<p>The one-hour episodes will feature the biggest names from NXT UK, including Pete Dunne, Mark Andrews, Rhea Ripley, Toni Storm, Tyler Bate, Trent Seven and Wolfgang. Joining the NXT UK broadcasting team as backstage interviewer is British broadcasting personality Radzi Chinyanganya, best known for hosting ITV game show "Cannonball," and in his ongoing role as a presenter of the world's longest-running children's TV show, the BBC's "Blue Peter." Calling the action are commentators Nigel McGuinness and Vic Joseph, joined by ring announcer Andy Shepherd and NXT UK General Manager, the legendary Johnny Saint.</p>1.652005e+09https://api.tvmaze.com/episodes/1976649
1252005096https://www.tvmaze.com/episodes/2005096/carl-webers-the-family-business-2x07-take-no-prisonersTake No Prisoners2.07.0regular2020-12-3121:002021-01-01T02:00:00+00:0060.0<p>Brother X has missed on his attempt to kill LC and Chippy. However things are far from over now that he unknowingly has captured a Duncan.</p>39441https://www.tvmaze.com/shows/39441/carl-webers-the-family-businessCarl Weber's The Family BusinessScriptedEnglish['Drama', 'Crime', 'Thriller']Running60.060.02018-11-13nanhttps://www.bet.plus/shows/the-family-business93.0nan<p>Meet the Duncans, a prominent family from Jamaica, Queens living fast and luxurious. By day, they're an upstanding family; by night, they live a dangerous secret life. The patriarch of the family, L.C. Duncan, is at the age when he's starting to think about retirement and has to decide which of his children should take over his thriving exotic car dealership. The Duncans quickly come under siege from some of the top politicians, mafia and drug cartels in the city. The Duncans will have to stick together or die separately.</p>1.638412e+09https://api.tvmaze.com/episodes/2005096
1262005098https://www.tvmaze.com/episodes/2005098/carl-webers-the-family-business-2x08-the-nuclear-optionThe Nuclear Option2.08.0regular2020-12-3121:002021-01-01T02:00:00+00:0060.0<p>The war heats up as the Duncans deploy the nuclear option, shutting down HEAT and recruiting allies. Paris finally takes responsibility for her actions.</p>39441https://www.tvmaze.com/shows/39441/carl-webers-the-family-businessCarl Weber's The Family BusinessScriptedEnglish['Drama', 'Crime', 'Thriller']Running60.060.02018-11-13nanhttps://www.bet.plus/shows/the-family-business93.0nan<p>Meet the Duncans, a prominent family from Jamaica, Queens living fast and luxurious. By day, they're an upstanding family; by night, they live a dangerous secret life. The patriarch of the family, L.C. Duncan, is at the age when he's starting to think about retirement and has to decide which of his children should take over his thriving exotic car dealership. The Duncans quickly come under siege from some of the top politicians, mafia and drug cartels in the city. The Duncans will have to stick together or die separately.</p>1.638412e+09https://api.tvmaze.com/episodes/2005098
1272005099https://www.tvmaze.com/episodes/2005099/carl-webers-the-family-business-2x09-mothers-and-their-sonsMothers and Their Sons2.09.0regular2020-12-3121:002021-01-01T02:00:00+00:0060.0<p>Junior and Sonya make landfall in Cuba. Chippy and Nevada take a trip. Consuela seeks protection from her husband Alejandro. Who is Sage?</p><p> </p><p><br /> </p>39441https://www.tvmaze.com/shows/39441/carl-webers-the-family-businessCarl Weber's The Family BusinessScriptedEnglish['Drama', 'Crime', 'Thriller']Running60.060.02018-11-13nanhttps://www.bet.plus/shows/the-family-business93.0nan<p>Meet the Duncans, a prominent family from Jamaica, Queens living fast and luxurious. By day, they're an upstanding family; by night, they live a dangerous secret life. The patriarch of the family, L.C. Duncan, is at the age when he's starting to think about retirement and has to decide which of his children should take over his thriving exotic car dealership. The Duncans quickly come under siege from some of the top politicians, mafia and drug cartels in the city. The Duncans will have to stick together or die separately.</p>1.638412e+09https://api.tvmaze.com/episodes/2005099
1282005100https://www.tvmaze.com/episodes/2005100/carl-webers-the-family-business-2x10-i-dont-want-to-lose-my-queenI Don't Want to Lose My Queen2.010.0regular2020-12-3121:002021-01-01T02:00:00+00:0060.0<p>Brother X begins to unravel. Sasha and Elijah play an enlightening game of chess. What does Billionaire Alexander Cora want?</p><p> </p><p><br /> </p>39441https://www.tvmaze.com/shows/39441/carl-webers-the-family-businessCarl Weber's The Family BusinessScriptedEnglish['Drama', 'Crime', 'Thriller']Running60.060.02018-11-13nanhttps://www.bet.plus/shows/the-family-business93.0nan<p>Meet the Duncans, a prominent family from Jamaica, Queens living fast and luxurious. By day, they're an upstanding family; by night, they live a dangerous secret life. The patriarch of the family, L.C. Duncan, is at the age when he's starting to think about retirement and has to decide which of his children should take over his thriving exotic car dealership. The Duncans quickly come under siege from some of the top politicians, mafia and drug cartels in the city. The Duncans will have to stick together or die separately.</p>1.638412e+09https://api.tvmaze.com/episodes/2005100
1292005101https://www.tvmaze.com/episodes/2005101/carl-webers-the-family-business-2x11-deploy-the-dronesDeploy the Drones2.011.0regular2020-12-3121:002021-01-01T02:00:00+00:0060.0<p>The war between the Duncans and Brother X is finally coming to a conclusion and the Duncans are back to business as usual.</p>39441https://www.tvmaze.com/shows/39441/carl-webers-the-family-businessCarl Weber's The Family BusinessScriptedEnglish['Drama', 'Crime', 'Thriller']Running60.060.02018-11-13nanhttps://www.bet.plus/shows/the-family-business93.0nan<p>Meet the Duncans, a prominent family from Jamaica, Queens living fast and luxurious. By day, they're an upstanding family; by night, they live a dangerous secret life. The patriarch of the family, L.C. Duncan, is at the age when he's starting to think about retirement and has to decide which of his children should take over his thriving exotic car dealership. The Duncans quickly come under siege from some of the top politicians, mafia and drug cartels in the city. The Duncans will have to stick together or die separately.</p>1.638412e+09https://api.tvmaze.com/episodes/2005101
1302005102https://www.tvmaze.com/episodes/2005102/carl-webers-the-family-business-2x12-the-hunt-for-brother-xThe Hunt for Brother X2.012.0regular2020-12-3121:002021-01-01T02:00:00+00:0060.0<p>LC must decide Elijah's fate as his sons hunt down a deranged Brother X. Meanwhile Billionaire Alexander Cora plots against the unsuspecting Duncans to takeover HEAT.</p>39441https://www.tvmaze.com/shows/39441/carl-webers-the-family-businessCarl Weber's The Family BusinessScriptedEnglish['Drama', 'Crime', 'Thriller']Running60.060.02018-11-13nanhttps://www.bet.plus/shows/the-family-business93.0nan<p>Meet the Duncans, a prominent family from Jamaica, Queens living fast and luxurious. By day, they're an upstanding family; by night, they live a dangerous secret life. The patriarch of the family, L.C. Duncan, is at the age when he's starting to think about retirement and has to decide which of his children should take over his thriving exotic car dealership. The Duncans quickly come under siege from some of the top politicians, mafia and drug cartels in the city. The Duncans will have to stick together or die separately.</p>1.638412e+09https://api.tvmaze.com/episodes/2005102